Systems and methods for statistical control and fault detection in a building management system

ABSTRACT

A building management strategy includes using exponentially weighted moving averages with statistical models to detect changes in the behavior of the building management system. Detected changes in the behavior of the system may indicate a detected fault, a change in a predicted behavior, or a need for the statistical models to be updated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. application Ser. No. 12/949,660, filedNov. 18, 2010, which is a continuation-in-part of U.S.continuation-in-part application Ser. No. 12/819,977, filed Jun. 21,2010, which claims the benefit of U.S. Provisional Application No.61/219,326, filed Jun. 22, 2009, U.S. Provisional Application No.61/234,217, filed Aug. 14, 2009, and U.S. Provisional Application No.61/302,854, filed Feb. 9, 2010. The entireties of U.S. application Ser.No. 12/819,977 and U.S. Provisional Application Nos. 61/302,854,61/219,326, and 61/234,217 are hereby incorporated by reference.

BACKGROUND

The present invention relates generally to the field of buildingmanagement systems. The present invention more particularly relates tosystems and methods for statistical control and fault detection in abuilding management system.

Conventional building management systems utilize a variety of controland fault detection methods. Conventional building management systemsuse simple sensor thresholds to conduct building management system(e.g., HVAC) control and fault detection tasks. To the extentstatistical analysis is used in conventional building managementsystems, it is typically rather inflexible as developing appropriatestatistical models can be challenging and difficult.

SUMMARY

One embodiment of the invention relates to a controller for a buildingmanagement system that includes a processing circuit configured to atleast one of receive or calculate an updated performance value for thebuilding management system. The processing circuit is also configured tomaintain at least one threshold parameter relative to a history ofperformance values. The processing circuit is further configured todetermine an exponentially weighted moving average of the performancevalues. The processing circuit is yet further configured to determinewhether the moving average of the performance values is statisticallysignificant by comparing the moving average to the at least onethreshold parameter. The processing circuit is also configured togenerate an output that indicates a determination of statisticalsignificance.

The threshold parameter in the controller may be maintained based atleast in part on estimators of scale of the history of performancevalues. For example, the threshold parameter may be calculated bymultiplying an estimator of scale of the history by a constant value andat least one of adding or subtracting the result of the multiplicationto the median of the history. In an exemplary embodiment, the estimatorof scale may be selected to correspond with a Gaussian efficiency ofabout 58%. In another exemplary embodiment the estimator of scale may beselected to correspond with a Gaussian efficiency of about 82%. Thethreshold parameter may be a threshold for a predicted power consumptionand the performance values comprise measured power consumptions. Inother embodiments, the processing circuit may also be configured toupdate the threshold parameter automatically and without user input orconfigured to cause a graphical user interface to be displayed on anelectronic display device, wherein the graphical user interfacecomprises indicia that a fault has occurred.

Another embodiment of the invention relates to a method of detectingfaults in a building management system. The method includes, at acomputer of the building management system, receiving first performancevalues for the building management system. The method also includescalculating an exponentially weighted moving average using theperformance values. The method further includes determining if themoving average is statistically significant by comparing the movingaverage to a threshold parameter. The method yet further includesreceiving new performance values for the building management system andupdating the threshold parameter using the new performance values.

In one embodiment, the computer is used to generate at least onethreshold parameter by generating a target parameter that is the medianof a history of performance values, generating an estimator of scale ofthe history that reduces the effects of outliers in the history ofperformance values, and using the estimator of scale to generate thethreshold parameter and storing the threshold parameter in a memory ofthe computer. The threshold parameter may also be generated bydetermining if a history of performance values is autocorrelated,applying an autoregressive model to the history and using theautoregressive model to generate the threshold parameter. In oneembodiment, the method also includes adjusting the threshold parameterautomatically and without user input, if the new moving average isstatistically significant. In another embodiment, an anti-spike filteris applied to the new performance values. In yet another embodiment, thenew performance values are power consumptions and the at least onethreshold parameter models a predicted power consumption.

Another embodiment of the invention relates to a building managementsystem having a processing circuit configured to receive firstperformance values and to generate an exponentially weighted movingaverage using the first performance values. The processing circuit isalso configured to determine whether the moving average is statisticallysignificant by comparing the moving average to the at least onethreshold parameter. The processing circuit is also configured to updatethe threshold parameter if the moving average is determined to bestatistically significant.

Alternative exemplary embodiments relate to other features andcombinations of features as may be generally recited in the claims.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1A is a block diagram of a building manager connected to a smartgrid and a plurality of building subsystems, according to an exemplaryembodiment;

FIG. 1B is a more detailed block diagram of the building manager shownin FIG. 1A, according to an exemplary embodiment;

FIG. 2 is a block diagram of the building subsystem integration layershown in FIG. 1A, according to an exemplary embodiment;

FIG. 3 is a detailed diagram of a portion of a smart building manager asshown in FIGS. 1A and 1B, according to an exemplary embodiment;

FIG. 4 is a detailed diagram of a fault detection and diagnostics layeras shown in FIGS. 1A and 1B, according to an exemplary embodiment; and

FIG. 5A is a flow diagram of a process for using statistical processcontrol with moving averages, according to an exemplary embodiment;

FIG. 5B is a detailed diagram of a fault detection module, according toan exemplary embodiment;

FIG. 6A is a flow diagram of a process for generating a statisticalprocess control chart, according to an exemplary embodiment;

FIG. 6B is a more detailed flow diagram of a process for generating astatistical process control chart, according to an exemplary embodiment;

FIG. 7 is a detailed diagram of a training module for generating astatistical model, according to an exemplary embodiment;

FIG. 8 is a process for measuring and verifying energy savings in abuilding management system, according to an exemplary embodiment; and

FIG. 9 is a detailed diagram of a building management system usingstatistical control, according to an exemplary embodiment.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

The invention relates to a building management system configured toimprove building efficiency, to enable greater or improved use ofrenewable energy sources, and to provide more comfortable and productivebuildings.

A building management system (BMS) is, in general, hardware and/orsoftware configured to control, monitor, and manage devices in or arounda building or building area. BMS subsystems or devices can includeheating, ventilation, and air conditioning (HVAC) subsystems or devices,security subsystems or devices, lighting subsystems or devices, firealerting subsystems or devices, elevator subsystems or devices, otherdevices that are capable of managing building functions, or anycombination thereof.

Referring now to FIG. 1A, a block diagram of a system 100 including asmart building manager 106 is shown, according to an exemplaryembodiment. Smart building manager 106 is connected to a smart grid 104and a plurality of building subsystems 128. The building subsystems 128may include a building electrical subsystem 134, an informationcommunication technology (ICT) subsystem 136, a security subsystem 138,a HVAC subsystem 140, a lighting subsystem 142, a lift/escalatorssubsystem 132, and a fire safety subsystem 130. The building subsystems128 can include fewer, additional, or alternative subsystems. Forexample, building subsystems 128 may also or alternatively include arefrigeration subsystem, an advertising or signage subsystem, a cookingsubsystem, a vending subsystem, or a printer or copy service subsystem.Conventionally, these systems are autonomous and managed by separatecontrol systems. The smart building manager 106 described herein isconfigured to achieve energy consumption and energy demand reductions byintegrating the management of the building subsystems.

Each of building subsystems 128 includes any number of devices,controllers, and connections for completing its individual functions andcontrol activities. For example, HVAC subsystem 140 may include achiller, a boiler, any number of air handling units, economizers, fieldcontrollers, supervisory controllers, actuators, temperature sensors,and other devices for controlling the temperature within a building. Asanother example, lighting subsystem 142 may include any number of lightfixtures, ballasts, lighting sensors, dimmers, or other devicesconfigured to controllably adjust the amount of light provided to abuilding space. Security subsystem 138 may include occupancy sensors,video surveillance cameras, digital video recorders, video processingservers, intrusion detection devices, access control devices andservers, or other security-related devices.

In an exemplary embodiment, the smart building manager 106 is configuredto include a communications interface 107 to the smart grid 104 outsidethe building, an interface 109 to disparate subsystems 128 within abuilding (e.g., HVAC, lighting security, lifts, power distribution,business, etc.), and an interface to applications 120, 124 (network orlocal) for allowing user control and the monitoring and adjustment ofthe smart building manager 106 or subsystems 128. Enterprise controlapplications 124 may be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 124 may also oralternatively be configured to provide configuration GUIs forconfiguring the smart building manager 106. In yet other embodiments,enterprise control applications 124 can work with layers 110-118 tooptimize building performance (e.g., efficiency, energy use, comfort, orsafety) based on inputs received at the interface 107 to the smart gridand the interface 109 to building subsystems 128. In an exemplaryembodiment, smart building manager 106 is integrated within a singlecomputer (e.g., one server, one housing, etc.). In various otherexemplary embodiments the smart building manager 106 can be distributedacross multiple servers or computers (e.g., that can exist indistributed locations).

FIG. 1B illustrates a more detailed view of smart building manager 106,according to an exemplary embodiment. In particular, FIG. 1B illustratessmart building manager 106 as having a processing circuit 152.Processing circuit 152 is shown to include a processor 154 and memorydevice 156. Processor 154 can be implemented as a general purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable electronic processing components. Memorydevice 156 (e.g., memory, memory unit, storage device, etc.) is one ormore devices (e.g., RAM, ROM, Flash memory, hard disk storage, etc.) forstoring data and/or computer code for completing or facilitating thevarious processes, layers and modules described in the presentapplication. Memory device 156 may be or include volatile memory ornon-volatile memory. Memory device 156 may include database components,object code components, script components, or any other type ofinformation structure for supporting the various activities andinformation structures described in the present application. Accordingto an exemplary embodiment, memory device 156 is communicably connectedto processor 154 via processing circuit 152 and includes computer codefor executing (e.g., by processing circuit 152 and/or processor 154) oneor more processes described herein.

Communications interfaces 107, 109 can be or include wired or wirelessinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith, e.g., smart grid 104, energy providers and purchasers 102,building subsystems 128, or other external sources via a directconnection or a network connection (e.g., an Internet connection, a LAN,WAN, or WLAN connection, etc.). For example, communications interfaces107, 109 can include an Ethernet card and port for sending and receivingdata via an Ethernet-based communications link or network. In anotherexample, communications interfaces 107, 109 can include a WiFitransceiver for communicating via a wireless communications network. Inanother example, one or both of interfaces 107, 109 may include cellularor mobile phone communications transceivers. In one embodiment,communications interface 107 is a power line communications interfaceand communications interface 109 is an Ethernet interface. In otherembodiments, both communications interface 107 and communicationsinterface 109 are Ethernet interfaces or are the same Ethernetinterface. Further, while FIG. 1A shows applications 120 and 124 asexisting outside of smart building manager 106, in some embodiments,applications 120 and 124 may be hosted within smart building manager 106generally or memory device 156 more particularly.

Building Subsystem Integration Layer

Referring further to FIG. 1B, the building subsystem integration layer118 is configured to manage communications between the rest of the smartbuilding manager 106's components and the building subsystems. Thebuilding subsystem integration layer 118 may also be configured tomanage communications between building subsystems. The buildingsubsystem integration layer 118 may be configured to translatecommunications (e.g., sensor data, input signals, output signals, etc.)across a plurality of multi-vendor/multi-protocol systems. For example,the building subsystem integration layer 118 may be configured tointegrate data from subsystems 128.

In FIG. 2, the building subsystem integration layer 118 is shown ingreater detail to include a message format and content normalizationcomponent 202. The message format and content normalization component202 is configured to convert data messages for and from disparatelyprotocolled devices or networks (e.g., different building subsystems,differently protocolled smart-grid sources, etc.). The message formatand content normalization component 202 is shown to include twosubcomponents, an application normalization component 204 and a buildingsubsystem normalization component 206. The application normalizationcomponent 204 is a computer function, object, service, or combinationthereof configured to drive the conversion of communications for andfrom applications (e.g., enterprise level applications 120, 124 shown inFIG. 1A, a computerized maintenance management system 222, utilitycompany applications via smart grid 104 shown in FIG. 1A, etc.). Thebuilding subsystem normalization component 206 is a computer function,object, service, or combination thereof configured to drive theconversion of communications for and from building subsystems (e.g.,building subsystems 128 shown in FIG. 1A, building subsystemcontrollers, building devices, security systems, fire systems, etc.).The application normalization component 204 and the building subsystemnormalization component 206 are configured to accommodate multiplecommunications or data protocols. In some embodiments, the applicationnormalization component 204 and the building subsystem normalizationcomponent 206 are configured to conduct the conversion for each protocolbased on information stored in modules 208-220 (e.g., a table, a script,in memory device 156 shown in FIG. 1B) for each of systems or devices222-234. The protocol modules 208-220 may be, for example, schema mapsor other descriptions of how a message for one protocol should betranslated to a message for a second protocol. In some embodiments themodules 208-220 may be “plug-in” drivers that can be easily installed toor removed from a building subsystem integration layer 118 (e.g., via anexecutable installation routine, by placing a file in an interfacesfolder, etc.) during setup. For example, modules 208-220 may be vendorspecific (e.g., Johnson Controls, Honeywell, Siemens, etc.),standards-based (e.g., BACnet, ANSI C12.19, Lon Works, Modbus, RIP,SNMP, SOAP, web services, HTML, HTTP/HTTPS, XML, XAML, TFTP, DHCP, DNS,SMTP, SNTP, etc.), user built, user selected, or user customized. Insome embodiments the application normalization component 204 or buildingsubsystem normalization component 206 are configured for compatibilitywith new modules or drivers (e.g., user defined or provided by a vendoror third party). In such embodiments, message format and contentnormalization component 202 may advantageously be scaled for futureapplications or case-specific requirements (e.g., situations calling forthe use of additional cyber security standards such as dataencryption/decryption) by changing the active module set or byinstalling a new module.

Using message format and content normalization component 202, thebuilding subsystem integration layer 118 can be configured to provide aservice-oriented architecture for providing cross-subsystem controlactivities and cross-subsystem applications. The message format andcontent normalization component 202 can be configured to provide arelatively small number of straightforward interfaces (e.g., applicationprogramming interfaces (APIs)) or protocols (e.g., open protocols,unified protocols, common protocols) for use by layers 108-116 (shown inFIG. 1A) or external applications (e.g., 120, 124 shown in FIG. 1A) andto “hide” such layers or applications from the complexities of theunderlying subsystems and their particular data transport protocols,data formats, semantics, interaction styles, and the like. Configurationof the message format and content normalization component 202 may occurautomatically (e.g., via a building subsystem and device discoveryprocess), via user configuration, or by a combination of automateddiscovery and user configuration. User configuration may be driven byproviding one or more graphical user interfaces or “wizards” to a user,the graphical user interfaces allowing the user to map an attribute fromone protocol to an attribute of another protocol. Configuration tool 162shown in FIG. 1B may be configured to drive such an association process.The configuration tool 162 may be served to clients (local or remote)via web services 158 and/or GUI engine 160 (both shown in FIG. 1B). Theconfiguration tool 162 may be provided as a thin web client (e.g., thatprimarily interfaces with web services 158) or a thick client (e.g.,that only occasionally draws upon web services 158 and/or GUI engine160). Configuration tool 162 may be configured to use a W3C standardintended to harmonize semantic information from different systems tocontrollably define, describe and store relationships between thedata/protocols (e.g., define the modules 208-220). For example, the W3Cstandard used may be the Web Ontology Language (OWL). In some exemplaryembodiments, configuration tool 162 may be configured to prepare themessage format and content normalization component 202 (anddevice/protocol modules 208-220 thereof) for machine levelinteroperability of data content.

Once the building subsystem integration layer 118 is configured,developers of applications may be provided with a software developmentkit to allow rapid development of applications compatible with the smartbuilding manager (e.g., with an application-facing protocol or API ofthe building subsystem integration layer). Such an API orapplication-facing protocol may be exposed at the enterprise integrationlayer 108 shown in FIGS. 1A and 1B. In various exemplary embodiments,the smart building manager 106 including building subsystem integrationlayer 118 includes the following features or advantages: it is seamlessin that heterogeneous applications and subsystems may be integratedwithout varying or affecting the behavior of the external facinginterfaces or logic; it is open in that it allows venders to developproducts and applications by coding adapters (e.g. modules 208-220 shownin FIG. 2) or features according to a well-defined specification; it ismulti-standard in that it supports subsystems that operate according tostandards as well as proprietary protocols; it is extensible in that itaccommodates new applications and subsystems with little to nomodification; it is scalable in that it supports many applications andsubsystems; it is adaptable in that it allows for the addition ordeletion of applications or subsystems without affecting systemconsistency; it is user-configurable in that it is adjustable to changesin the business environment, business rules, or business workflows; andit is secure in that it protects information transferred through theintegration channel. Additional details with respect to buildingsubsystem integration layer 118 are described below with respect to FIG.3.

Integrated Control Layer

Referring further to FIGS. 1A and 1B, the integrated control layer 116is configured to use the data input or output of the building subsystemintegration layer 118 to make control decisions. Due to the subsystemintegration provided by the building subsystem integration layer 118,the integrated control layer 116 can integrate control activities of thesubsystems 128 such that the subsystems 128 behave as a singleintegrated supersystem. In an exemplary embodiment, the integratedcontrol layer 116 includes control logic that uses inputs and outputsfrom a plurality of building subsystems to provide greater comfort andenergy savings relative to the comfort and energy savings that separatesubsystems could provide alone. For example, information from a firstbuilding subsystem may be used to control a second building subsystem.By way of a more particular example, when a building employee badges inat a parking garage, a message may be sent from the parking subsystem tothe building subsystem integration layer 118, converted into an eventrecognized as a universal occupancy (e.g., “badge-in”) event andprovided to integrated control layer 116. Integrated control layer 116may include logic that turns on the lights in the building employee'soffice, begins cooling the building employee's office in response to theanticipated occupancy, and boots up the employee's computer. Thedecision to turn the devices on is made by integrated control layer 116and integrated control layer 116 may cause proper “on” commands to beforwarded to the particular subsystems (e.g., the lighting subsystem,the IT subsystem, the HVAC subsystem). The integrated control layer 116passes the “on” commands through building subsystem integration layer118 so that the messages are properly formatted or protocolled forreceipt and action by the subsystems. As is illustrated in FIGS. 1A-B,the integrated control layer 116 is logically above the buildingsubsystems and building subsystem controllers. The integrated controllayer 116, by having access to information from multiple systems, isconfigured to use inputs from one or more building subsystems 128 tomake control decisions for control algorithms of other buildingsubsystems. For example, the “badge-in” event described above can beused by the integrated control layer 116 (e.g., a control algorithmthereof) to provide new setpoints to an HVAC control algorithm of theHVAC subsystem.

While conventional building subsystem controllers are only able toprocess inputs that are directly relevant to the performance of theirown control loops, the integrated control layer 116 is configured to usean input from a first subsystem to make an energy-saving controldecision for a second subsystem. Results of these decisions can becommunicated back to the building subsystem integration layer 116 via,for example, the message format and content normalization component 202shown in FIG. 2A. Therefore, advantageously, regardless of theparticular HVAC system or systems connected to the smart buildingmanager, and due to the normalization at the building subsystemintegration layer 118, the integrated control layer's control algorithmscan determine a control strategy using normalized temperature inputs,and provide an output including a normalized setpoint temperature to thebuilding subsystem integration layer. The building subsystem integrationlayer 118 can translate the normalized setpoint temperature into acommand specific to the building subsystem or controller for which thesetpoint adjustment is intended. If multiple subsystems are utilized tocomplete the same function (e.g., if multiple disparately protocolledHVAC subsystems are provided in different regions of a building), thebuilding subsystem integration layer 118 can convert a command decision(e.g., to lower the temperature setpoint by 2 degrees) to multipledifferent commands for receipt and action by the multiple disparatelyprotocolled HVAC subsystems. In this way, functions of the integratedcontrol layer 116 may be executed using the capabilities of buildingsubsystem integration layer 118. In an exemplary embodiment, theintegrated control layer is configured to conduct the primary monitoringof system and subsystem statuses and interrelationships for thebuilding. Such monitoring can cross the major energy consumingsubsystems of a building to allow for cross-subsystem energy savings tobe achieved (e.g., by the demand response layer 112).

The integrated control layer 116 is shown to be logically below thedemand response layer 112. The integrated control layer 116 isconfigured to enhance the effectiveness of the demand response layer 112by enabling building subsystems 128 and their respective control loopsto be controlled in coordination with the demand response layer 112.This configuration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, the integratedcontrol layer 116 may be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller. The integrated control layer116 may also be configured to provide feedback to the demand responselayer 112 so that the demand response layer 112 checks that constraints(e.g., temperature, lighting levels, etc.) are properly maintained evenwhile demanded load shedding is in progress. The constraints may alsoinclude setpoint or sensed boundaries relating to safety, equipmentoperating limits and performance, comfort, fire codes, electrical codes,energy codes, and the like. The integrated control layer 116 is alsologically below the fault detection and diagnostics layer 114 and theautomated measurement and validation layer 110. The integrated controllayer may be configured to provide calculated inputs (e.g.,aggregations) to these “higher levels” based on outputs from more thanone building subsystem.

Control activities that may be completed by the integrated control layer116 (e.g., software modules or control algorithms thereof) includeoccupancy-based control activities. Security systems such as radiofrequency location systems (RFLS), access control systems, and videosurveillance systems can provide detailed occupancy information to theintegrated control layer 116 and other building subsystems 128 via thesmart building manager 106 (and more particularly, via the buildingsubsystem integration layer 118). Integration of an access controlsubsystem and a security subsystem for a building may provide detailedoccupancy data for consumption by the integrated control layer 116(e.g., beyond binary “occupied” or “unoccupied” data available to someconventional HVAC systems that rely on, for example, a motion sensor).For example, the exact number of occupants in the building (or buildingzone, floor, conference room, etc.) may be provided to the integratedcontrol layer 116 or aggregated by the integrated control layer 116using inputs from a plurality of subsystems. The exact number ofoccupants in the building can be used by the integrated control layer116 to determine and command appropriate adjustments for buildingsubsystems 128 (such as HVAC subsystem 140 or lighting subsystem 142).Integrated control layer 116 may be configured to use the number ofoccupants, for example, to determine how many of the available elevatorsto activate in a building. If the building is only 20% occupied, theintegrated control layer 116, for example, may be configured to powerdown 80% of the available elevators for energy savings. Further,occupancy data may be associated with individual workspaces (e.g.,cubicles, offices, desks, workstations, etc.) and if a workspace isdetermined to be unoccupied by the integrated control layer, a controlalgorithm of the integrated control layer 116 may allow for the energyusing devices serving the workspace to be turned off or commanded toenter a low power mode. For example, workspace plug-loads, tasklighting, computers, and even phone circuits may be affected based on adetermination by the integrated control layer that the employeeassociated with the workspace is on vacation (e.g., using data inputsreceived from a human-resources subsystem). Significant electrical loadsmay be shed by the integrated control layer 116, including, for example,heating and humidification loads, cooling and dehumidification loads,ventilation and fan loads, electric lighting and plug loads (e.g. withsecondary thermal loads), electric elevator loads, and the like. Theintegrated control layer 116 may further be configured to integrate anHVAC subsystem or a lighting subsystem with sunlight shading devices orother “smart window” technologies. Natural day-lighting cansignificantly offset lighting loads but for optimal comfort may becontrolled by the integrated control layer to prevent glare orover-lighting. Conversely, shading devices and smart windows may also becontrolled by the integrated control layer 116 to calculably reducesolar heat gains in a building space, which can have a significantimpact on cooling loads. Using feedback from sensors in the space, andwith knowledge of the HVAC control strategy, the integrated controllayer 116 may further be configured to control the transmission ofinfrared radiation into the building, minimizing thermal transmissionwhen the HVAC subsystem is cooling and maximizing thermal transmissionwhen the HVAC subsystem is heating. As a further example of anoccupancy-based control strategy that may be implemented by theintegrated control layer 116, inputs from a video security subsystem maybe analyzed by a control algorithm of the integrated control layer 116to make a determination regarding occupancy of a building space. Usingthe determination, the control algorithm may turn off the lights, adjustHVAC set points, power-down ICT devices serving the space, reduceventilation, and the like, enabling energy savings with an acceptableloss of comfort to occupants of the building space.

Referring now to FIG. 3, a detailed diagram of a portion of smartbuilding manager 106 is shown, according to an exemplary embodiment. Inparticular, FIG. 3 illustrates a detailed embodiment of integratedcontrol layer 116. Configuration tools 162 can allow a user to define(e.g., via graphical user interfaces, via prompt-driven “wizards,” etc.)how the integrated control layer 116 should react to changing conditionsin the building subsystems 128. In an exemplary embodiment,configuration tools 162 allow a user to build and storecondition-response scenarios that can cross multiple building subsystemsand multiple enterprise control applications (e.g., work ordermanagement system applications, entity resource planning (ERP)applications, etc.).

Building subsystems 128, external sources such as smart grid 104, andinternal layers such as demand response layer 112 can regularly generateevents (e.g., messages, alarms, changed values, etc.) and provide theevents to integrated control layer 116 or another layer configured tohandle the particular event. For example, demand response (DR) events(e.g., a change in real time energy pricing) may be provided to smartbuilding manager 106 as Open Automated Demand Response (“OpenADR”)messages (a protocol developed by Lawrence Berkeley NationalLaboratories). The DR messages may be received by OpenADR adapter 306(which may be a part of enterprise application layer 108 shown in FIGS.1A and 1B). The OpenADR adapter 306 may be configured to convert theOpenADR message into a DR event configured to be understood (e.g.,parsed, interpreted, processed, etc.) by demand response layer 112. TheDR event may be formatted and transmitted according to or via a servicebus 302 for the smart building manager 106.

Service bus adapter 304 may be configured to “trap” or otherwise receivethe DR event on the service bus 302 and forward the DR event on todemand response layer 112. Service bus adapter 304 may be configured toqueue, mediate, or otherwise manage demand response messages for demandresponse layer 112. Once a DR event is received by demand response layer112, logic thereof can generate a control trigger in response toprocessing the DR event. The integrated control engine 308 of integratedcontrol layer 116 is configured to parse the received control trigger todetermine if a control strategy exists in control strategy database 310that corresponds to the received control trigger. If a control strategyexists, integrated control engine 308 executes the stored controlstrategy for the control trigger. In some cases the output of theintegrated control engine 308 will be an “apply policy” message forbusiness rules engine 312 to process. Business rules engine 312 mayprocess an “apply policy” message by looking up the policy in businessrules database 314. A policy in business rules database 314 may take theform of a set of action commands for sending to building subsystems 128.The set of action commands may include ordering or scripting forconducting the action commands at the correct timing, ordering, or withother particular parameters. When business rules engine 312 processesthe set of action commands, therefore, it can control the ordering,scripting, and other parameters of action commands transmitted to thebuilding subsystems 128.

Action commands may be commands for relatively direct consumption bybuilding subsystems 128, commands for other applications to process, orrelatively abstract cross-subsystem commands. Commands for relativelydirect consumption by building subsystems 128 can be passed throughservice bus adapter 322 to service bus 302 and to a subsystem adapter314 for providing to a building subsystem in a format particular to thebuilding subsystem. Commands for other applications to process mayinclude commands for a user interface application to request feedbackfrom a user, a command to generate a work order via a computerizedmaintenance management system (CMMS) application, a command to generatea change in an ERP application, or other application level commands.

More abstract cross-subsystem commands may be passed to a semanticmediator 316 which performs the task of translating those actions to thespecific commands required by the various building subsystems 128. Forexample, a policy might contain an abstract action to “set lighting zoneX to maximum light.” The semantic mediator 316 may translate this actionto a first command such as “set level to 100% for lighting object O incontroller C” and a second command of “set lights to on in controller Z,zone_id_no 3141593.” In this example both lighting object O incontroller C and zone_id_no 3141593 in controller Z may affect lightingin zone X. Controller C may be a dimming controller for accent lightingwhile controller Z may be a non-dimming controller for the primarylighting in the room. The semantic mediator 316 is configured todetermine the controllers that relate to zone X using ontology database320. Ontology database 320 stores a representation or representations ofrelationships (the ontology) between building spaces and subsystemelements and subsystems elements and concepts of the integrated buildingsupersystem. Using the ontology stored in ontology database 320, thesemantic mediator can also determine that controller C is dimming andrequires a numerical percentage parameter while controller Z is notdimming and requires only an on or off command. Configuration tool 162can allow a user to build the ontology of ontology database 320 byestablishing relationships between subsystems, building spaces,input/output points, or other concepts/objects of the buildingsubsystems and the building space.

Events other than those received via OpenADR adapter 306, demandresponse layer 112, or any other specific event-handing mechanism can betrapped by subsystem adapter 314 (a part of building integrationsubsystem layer 318) and provided to a general event manager 330 viaservice bus 302 and a service bus adapter. By the time an event from abuilding subsystem 128 is received by event manager 330, it may havebeen converted into a unified event (i.e., “common event,” “standardizedevent”, etc.) by subsystem adapter 314 and/or other components ofbuilding subsystem integration layer 318 such as semantic mediator 316.The event manager 330 can utilize an event logic DB to lookup controltriggers, control trigger scripts, or control trigger sequences based onreceived unified events. Event manager 330 can provide control triggersto integrated control engine 308 as described above with respect todemand response layer 112. As events are received, they may be archivedin event history 332 by event manager 330. Similarly, demand responselayer 112 can store DR events in DR history 335. One or both of eventmanager 330 and demand response layer 112 may be configured to waituntil multi-event conditions are met (e.g., by processing data inhistory as new events are received). For example, demand response layer112 may include logic that does not act to reduce energy loads until aseries of two sequential energy price increases are received. In anexemplary embodiment event manager 330 may be configured to receive timeevents (e.g., from a calendaring system). Different time events can beassociated with different triggers in event logic database 333.

In an exemplary embodiment, the configuration tools 162 can be used tobuild event conditions or trigger conditions in event logic 333 orcontrol strategy database 310. For example, the configuration tools 162can provide the user with the ability to combine data (e.g., fromsubsystems, from event histories) using a variety of conditional logic.In varying exemplary embodiments, the conditional logic can range fromsimple logical operators between conditions (e.g., AND, OR, XOR, etc.)to pseudo-code constructs or complex programming language functions(allowing for more complex interactions, conditional statements, loops,etc.). The configuration tools 162 can present user interfaces forbuilding such conditional logic. The user interfaces may allow users todefine policies and responses graphically. In some embodiments, the userinterfaces may allow a user to select a pre-stored or pre-constructedpolicy and adapt it or enable it for use with their system.

Referring still to FIG. 3, in some embodiments, integrated control layer116 generally and integrated control engine 308 can operate as a“service” that can be used by higher level layers of smart buildingmanager 106, enterprise applications, or subsystem logic whenever apolicy or sequence of actions based on the occurrence of a condition isto be performed. In such embodiments, control operations do not need tobe reprogrammed. Instead, applications or logic can rely on theintegrated control layer 116 to receive an event and to execute therelated subsystem functions. For example, demand response layer 112,fault detection and diagnostics layer 114 (shown in FIGS. 1A and 1B),enterprise integration 108, and applications 120, 124 may all utilize ashared control strategy 310 and integrated control engine 308 toinitiate response sequences to events.

Fault Detection and Diagnostics Layer

Referring now to FIG. 4, the fault detection and diagnostics (FDD) layer114 is shown in greater detail, according to an exemplary embodiment.Fault detection and diagnostics (FDD) layer 114 is configured to provideon-going fault detection of building subsystems, building subsystemdevices, and control algorithms of the integrated control layer. The FDDlayer 114 may receive its inputs from the integrated control layer,directly from one or more building subsystems or devices, or from thesmart grid. The FDD layer 114 may automatically diagnose and respond todetected faults. The responses to detected or diagnosed faults mayinclude providing an alert message to a user, a maintenance schedulingsystem, or a control algorithm configured to attempt to repair the faultor to work-around the fault. In other exemplary embodiments FDD layer114 is configured to provide “fault” events to integrated control layeras described with reference to FIG. 3 and the integrated control layerof FIG. 3 is configured to execute control strategies and policies inresponse to the received fault events. According to an exemplaryembodiment, the FDD layer 114 (or a policy executed by an integratedcontrol engine or business rules engine) may shut-down systems or directcontrol activities around faulty devices or systems to reduce energywaste, extend equipment life, or assure proper control response. The FDDlayer 114 may be configured to use statistical analysis of nearreal-time or historical building subsystem data to rapidly identifyfaults in equipment operation.

As shown in FIG. 4, the FDD layer 114 is configured to store or access avariety of different system data stores (or data points for live data)402-410. FDD layer 114 may use some content of data stores 402-410 toidentify faults at the equipment level (e.g., specific chiller, specificAHU, specific terminal unit, etc.) and other content to identify faultsat component or subsystem levels. The FDD layer 114 may be configured tooutput a specific identification of the faulty component or cause of thefault (e.g., loose damper linkage) using detailed subsystem inputsavailable at the building subsystem integration layer (shown in previousFigures). Such specificity and determinations may be calculated by theFDD layer 114 based on such subsystem inputs and, for example,statistical fault detection module 412. Statistical fault detectionmodule 412 can utilize pattern recognition methods, patternclassification methods, rule-based classification methods, outlieranalysis, statistical quality control charting techniques, or the liketo conduct its statistical analysis. In some embodiments statisticalfault detection module 412 more particularly is configured to calculateor update performance indices 410. Performance indices 410 may becalculated based on exponentially-weighted moving averages (EWMAs) toprovide statistical analysis features which allow outlier andstatistical process control (SPC) techniques to be used to identifyfaults. For example, the FDD layer 114 may be configured to use meterdata 402 outliers to detect when energy consumption becomes abnormal.Statistical fault detection module 412 may also or alternatively beconfigured to analyze the meter data 402 using statistical methods thatprovide for data clustering, outlier analysis, or quality controldeterminations. The meter data 402 may be received from, for example, asmart meter, a utility, or calculated based on the building-use dataavailable to the smart building manager.

Once a fault is detected by the FDD layer 114 (e.g., by statisticalfault detection module 412), the FDD layer 114 may be configured togenerate one or more alarms or events to prompt manual fault diagnosticsor to initiate an automatic fault diagnostics activity via automateddiagnostics module 414. Automatic fault diagnostics module 414 may beconfigured to use meter data 402, weather data 404, model data 406(e.g., performance models based on historical building equipmentperformance), building subsystem data 408, performance indices 410, orother data available at the building subsystem integration layer tocomplete its fault diagnostics activities.

In an exemplary embodiment, when a fault is detected, the automateddiagnostics module 414 is configured to investigate the fault byinitiating expanded data logging and error detection/diagnosticsactivities relative to the inputs, outputs, and systems related to thefault. For example, the automated diagnostics module 414 may beconfigured to poll sensors associated with an air handling unit (AHU)(e.g., temperature sensors for the space served by the AHU, air flowsensors, position sensors, etc.) on a frequent or more synchronizedbasis to better diagnose the source of a detected AHU fault.

Automated fault diagnostics module 414 may further be configured tocompute residuals (differences between measured and expected values) foranalysis to determine the fault source. For example, automated faultdiagnostics module 414 may be configured to implement processingcircuits or methods described in U.S. patent application Ser. No.12/487,594, filed Jun. 18, 2009, titled “Systems and Methods for FaultDetection of Air Handling Units,” the entirety of which is incorporatedherein by reference. Automated fault diagnostics module 414 can use afinite state machine and input from system sensors (e.g., temperaturesensors, air mass sensors, etc.) to diagnose faults. State transitionfrequency (e.g., between a heating state, a free cooling state, and amechanical cooling state) may also be used by the statistical faultdetection module 412 and the automated diagnostics module 414 toidentify and diagnose unstable control issues. The FDD layer 114 mayalso or alternatively be configured for rule-based predictive detectionand diagnostics (e.g., to determine rule thresholds, to provide forcontinuous monitoring and diagnostics of building equipment).

In addition to or as an alternative to an automated diagnostics processprovided by automated diagnostics module 414, FDD layer 114 can drive auser through a manual diagnostic process using manual diagnostics module416. One or both of automated diagnostics module 414 and manualdiagnostics module 416 can store data regarding the fault and thediagnosis thereof for further assessment by manual or automated faultassessment engine 418. Any manually driven process of assessment engine418 can utilize graphical or textual user interfaces displayed to a userto receive feedback or input from a user. In some embodiments assessmentengine 418 will provide a number of possible reasons for a fault to theuser via a GUI. The user may select one of the faults for manualinvestigation or calculation. Similarly, an automated process ofassessment engine 418 may be configured to select the most probablecause for a fault based on diagnostics provided by modules 414 or 416.Once a cause is detected or estimated using assessment engine 418, awork order can be generated by work order generation and dispatchservice 420. Work order generation and dispatch service can transmit thework order to a service management system or a work dispatch service 420for action.

Data and processing results from modules 412, 414, 416, 418 or otherdata stored or modules of a fault detection and diagnostics layer can beprovided to the enterprise integration layer shown in FIGS. 1A and 1B.Monitoring and reporting applications 120 can then access the data or bepushed the data so that real time “system health” dashboards can beviewed and navigated by a user (e.g., a building engineer). For example,monitoring and reporting applications 120 may include a web-basedmonitoring application that includes several graphical user interface(GUI) elements (e.g., widgets, dashboard controls, windows, etc.) fordisplaying key performance indicators (KPI) or other information tousers of a GUI using FDD layer 114 information or analyses. In addition,the GUI elements may summarize relative energy use and intensity acrossdifferent buildings (real or modeled), different campuses, or the like.Other GUI elements or reports may be generated and shown based onavailable data that allow facility managers to assess performance acrossa group of buildings from one screen. The user interface or report (orunderlying data engine) may be configured to aggregate and categorizefaults by building, building type, equipment type, fault type, times ofoccurrence, frequency of occurrence, severity, and the like. The GUIelements may include charts or histograms that allow the user tovisually analyze the magnitude of occurrence of specific faults orequipment for a building, time frame, or other grouping. A “time series”pane of the GUI may allow users to diagnose a fault remotely byanalyzing and comparing interval time-series data, trends, and patternsfor various input/output points tracked/logged by the FDD layer 114. TheFDD layer 114 may include one or more GUI servers or services 422 (e.g.,a web service) to support such applications. Further, in someembodiments, applications and GUI engines may be included outside of theFDD layer 114 (e.g., monitoring and reporting applications 120 shown inFIG. 1A, web services 158 shown in FIG. 1B, GUI engine 160 shown in FIG.1B). The FDD layer 114 may be configured to maintain detailed historicaldatabases (e.g., relational databases, XML databases, etc.) of relevantdata and includes computer code modules that continuously, frequently,or infrequently query, aggregate, transform, search, or otherwiseprocess the data maintained in the detailed databases. The FDD layer 114may be configured to provide the results of any such processing to otherdatabases, tables, XML files, or other data structures for furtherquerying, calculation, or access by, for example, external monitoringand reporting applications.

In an exemplary embodiment, the automated diagnostics module 414automatically prioritizes detected faults. The prioritization may beconducted based on customer-defined criteria. The prioritization may beused by the manual or automated fault assessment module 418 to determinewhich faults to communicate to a human user via a dashboard or otherGUI. Further, the prioritization can be used by the work order dispatchservice to determine which faults are worthy of immediate investigationor which faults should be investigated during regular servicing ratherthan a special work request. The FDD layer 114 may be configured todetermine the prioritization based on the expected financial impact ofthe fault. The fault assessment module 418 may retrieve faultinformation and compare the fault information to historical information.Using the comparison, the fault assessment module 418 may determine anincreased energy consumption and use pricing information from the smartgrid to calculate the cost over time (e.g., cost per day). Each fault inthe system may be ranked according to cost or lost energy. The faultassessment module 418 may be configured to generate a report forsupporting operational decisions and capital requests. The report mayinclude the cost of allowing faults to persist, energy wasted due to thefault, potential cost to fix the fault (e.g., based on a serviceschedule), or other overall metrics such as overall subsystem orbuilding reliability (e.g., compared to a benchmark). The faultassessment module 418 may further be configured to conduct equipmenthierarchy-based suppression of faults (e.g., suppressed relative to auser interface, suppressed relative to further diagnostics, etc.). Forsuch suppression, module 318 may use the hierarchical informationavailable at, e.g., integrated control layer 116 or building subsystemintegration layer 318 shown in FIG. 3. For example, module 318 mayutilize building subsystem hierarchy information stored in ontologydatabase 320 to suppress lower level faults in favor of a higher levelfault (suppress faults for a particular temperature sensor and airhandling unit in favor of a fault that communicates “Inspect HVACComponents Serving Conference Room 30”).

FDD layer 114 may also receive inputs from lower level FDD processes.For example, FDD layer 114 may receive inputs from building subsystemsupervisory controllers or field controllers having FDD features. In anexemplary embodiment, FDD layer 114 may receive “FDD events,” processthe received FDD events, query the building subsystems for furtherinformation, or otherwise use the FDD events in an overall FDD scheme(e.g., prioritization and reporting). U.S. Pat. No. 6,223,544 (titled“Integrated Control and Fault Detection of HVAC Equipment,” issued May1, 2001) (incorporated herein by reference) and U.S. Pub. No.2009/0083583 (titled “Fault Detection Systems and Methods forSelf-Optimizing Heating, Ventilation, and Air Conditioning Controls”,filed Nov. 25, 2008, published Mar. 26, 2009) (incorporated herein byreference) may be referred to as examples of FDD systems and methodsthat may be implemented by FDD layer 114 (and/or lower level FDDprocesses for providing information to FDD layer 114).

Demand Response Layer

FIGS. 1A and 1B are further shown to include a demand response (DR)layer 112. The DR layer 112 is configured to optimize electrical demandin response to time-of-use prices, curtailment signals, or energyavailability. Data regarding time-of-use prices, energy availability,and curtailment signals may be received from the smart grid 104, fromenergy providers and purchasers 102 (e.g., an energy aggregator) via thesmart grid 104, from energy providers and purchasers 102 via acommunication network apart from the smart grid, from distributed energygeneration systems 122, from energy storage banks 126, or from othersources. According to an exemplary embodiment, the DR layer 112 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms in theintegrated control layer 116 to “load shed,” changing controlstrategies, changing setpoints, or shutting down building devices orsubsystems in a controlled manner. The architecture and process forsupporting DR events is shown in and described with reference to FIG. 3.The DR layer 112 may also include control logic configured to determinewhen to utilize stored energy based on information from the smart gridand information from a local or remote energy storage system. Forexample, when the DR layer 112 receives a message indicating risingenergy prices during a future “peak use” hour, the DR layer 112 candecide to begin using power from the energy storage system just prior tothe beginning of the “peak use” hour.

In some exemplary embodiments the DR layer 112 may include a controlmodule configured to actively initiate control actions (e.g.,automatically changing setpoints) which minimize energy costs based onone or more inputs representative of or based on demand (e.g., price, acurtailment signal, a demand level, etc.). The DR layer 112 may furtherinclude or draw upon one or more DR policy definitions (e.g., databases,XML files, etc.). The policy definitions may be edited or adjusted by auser (e.g., via a graphical user interface) so that the control actionsinitiated in response to demand inputs may be tailored for the user'sapplication, desired comfort level, particular building equipment, orbased on other concerns. For example, the DR policy definitions canspecify which equipment may be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a “high demand” setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.). One or more of the policies and control activities may be locatedwithin control strategy database 310 or business rules database 314.Further, as described above with reference to FIG. 3, some of the DRresponses to events may be processed and completed by integrated controllayer 116 with or without further inputs or processing by DR layer 112.

A plurality of market-based DR inputs and reliability based DR inputsmay be configured (e.g., via the DR policy definitions or other systemconfiguration mechanisms) for use by the DR layer 112. The smartbuilding manager 106 may be configured (e.g., self-configured, manuallyconfigured, configured via DR policy definitions, etc.) to select,deselect or differently weigh varying inputs in the DR layer'scalculation or execution of control strategies based on the inputs. DRlayer 112 may automatically (or via the user configuration) calculateoutputs or control strategies based on a balance of minimizing energycost and maximizing comfort. Such balance may be adjusted (e.g.,graphically, via rule sliders, etc.) by users of the smart buildingmanager via a configuration utility or administration GUI.

The DR layer 112 may be configured to receive inputs from other layers(e.g., the building subsystem integration layer, the integrated controllayer, etc.). The inputs received from other layers may includeenvironmental or sensor inputs such as temperature, carbon dioxidelevels, relative humidity levels, air quality sensor outputs, occupancysensor outputs, room schedules, and the like. The inputs may alsoinclude inputs such as electrical use (e.g., expressed in kWh), thermalload measurements, pricing information, projected pricing, smoothedpricing, curtailment signals from utilities, and the like from insidethe system, from the smart grid 104, or from other remote sources.

Some embodiments of the DR layer 112 may utilize industry standard“open” protocols or emerging National Institute of Standards andTechnology (NIST) standards to receive real-time pricing (RTP) orcurtailment signals from utilities or power retailers. In otherembodiments, proprietary protocols or other standards may be utilized.As mentioned above, in some exemplary embodiments, the DR layer 112 isconfigured to use the OpenADR protocol to receive curtailment signals orRTP data from utilities, other independent system operators (ISOs), orother smart grid sources. The DR layer 112, or another layer (e.g., theenterprise integration layer) that serves the DR layer 112 may beconfigured to use one or more security schemes or standards such as theOrganization for the Advancement of Structured Information Standards(OASIS) Web Service Security Standards to provide for securecommunications to/from the DR layer 112 and the smart grid 104 (e.g., autility company's data communications network). If the utility does notuse a standard protocol (e.g., the OpenADR protocol), the DR layer 112,the enterprise integration layer 108, or the building subsystemintegration layer 118 may be configured to translate the utility'sprotocol into a format for use by the utility. The DR layer 112 may beconfigured to bi-directionally communicate with the smart grid 104 orenergy providers and purchasers 102 (e.g., a utility, an energyretailer, a group of utilities, an energy broker, etc.) to exchangeprice information, demand information, curtailable load calculations(e.g., the amount of load calculated by the DR layer to be able to beshed without exceeding parameters defined by the system or user), loadprofile forecasts, and the like. DR layer 112 or an enterpriseapplication 120, 124 in communication with the DR layer 112 may beconfigured to continuously monitor pricing data provided byutilities/ISOs across the nation, to parse the useful information fromthe monitored data, and to display the useful information to a user toor send the information to other systems or layers (e.g., integratedcontrol layer 116).

The DR layer 112 may be configured to include one or more adjustablecontrol algorithms in addition to or as an alternative from allowing theuser creation of DR profiles. For example, one or more controlalgorithms may be automatically adjusted by the DR layer 112 usingdynamic programming or model predictive control modules. In oneembodiment, business rules engine 312 is configured to respond to a DRevent by adjusting a control algorithm or selecting a different controlalgorithm to use (e.g., for a lighting system, for an HVAC system, for acombination of multiple building subsystems, etc.).

The smart building manager 106 (e.g., using the demand response layer112) can be configured to automatically (or with the help of a user)manage energy spend. The smart building manager 106 (with input from theuser or operating using pre-configured business rules shown in FIG. 3)may be configured to accept time-of-use pricing signals or informationfrom a smart grid (e.g., an energy provider, a smart meter, etc.) and,using its knowledge of historical building system data, controlalgorithms, calendar information, and/or weather information receivedfrom a remote source, may be configured to conduct automatic costforecasting. The smart building manager 106 (e.g., the demand responselayer 112) may automatically (or with user approval) take specific loadshedding actions or control algorithm changes in response to differentcost forecasts.

The smart building manager 106 may also be configured to monitor andcontrol energy storage systems 126 (e.g., thermal, electrical, etc.) anddistributed generation systems 122 (e.g., a solar array for thebuilding, etc.). The smart building manager 106 or DR layer 112 may alsobe configured to model utility rates to make decisions for the system.All of the aforementioned processing activities or inputs may be used bythe smart building manager 106 (and more particularly, a demand responselayer 112 thereof) to limit, cap, profit-from, or otherwise manage thebuilding or campus's energy spend. For example, using time-of-usepricing information for an upcoming hour that indicates an unusuallyhigh price per kilowatt hour, the system may use its control of aplurality of building systems to limit cost without too drasticallyimpacting occupant comfort. To make such a decision and to conduct suchactivity, the smart building manager 106 may use data such as arelatively high load forecast for a building and information that energystorage levels or distributed energy levels are low. The smart buildingmanager 106 may accordingly adjust or select a control strategy toreduce ventilation levels provided to unoccupied areas, reduce serverload, raise a cooling setpoint throughout the building, reserve storedpower for use during the expensive period of time, dim lights inoccupied areas, turn off lights in unoccupied areas, and the like.

The smart building manager 106 may provide yet other services to improvebuilding or grid performance. For example, the smart building manager106 may provide for expanded user-driven load control (allowing abuilding manager to shed loads at a high level of system/devicegranularity). The smart building manager 106 may also monitor andcontrol power switching equipment to route power to/from the mostefficient sources or destinations. The smart building manager 106 maycommunicate to the power switching equipment within the building orcampus to conduct “smart” voltage regulation. For example, in the eventof a brownout, the smart building manager 106 may prioritize branches ofa building's internal power grid—tightly regulating and ensuring voltageto high priority equipment (e.g., communications equipment, data centerequipment, cooling equipment for a clean room or chemical factory, etc.)while allowing voltage to lower priority equipment to dip or be cut offby the smart grid (e.g., the power provider). The smart building manager106 or the DR layer 112 may plan these activities or proactively beginload shedding based on grid services capacity forecasting conducted by asource on the smart grid or by a local algorithm (e.g., an algorithm ofthe demand response layer). The smart building manager 106 or the DRlayer 112 may further include control logic for purchasing energy,selling energy, or otherwise participating in a real-time or nearreal-time energy market or auction. For example, if energy is predictedto be expensive during a time when the DR layer 112 determines it canshed extra load or perhaps even enter a net-positive energy state usingenergy generated by solar arrays, or other energy sources of thebuilding or campus, the DR layer 112 may offer units of energy duringthat period for sale back to the smart grid (e.g., directly to theutility, to another purchaser, in exchange for carbon credits, etc.).

In some exemplary embodiments, the DR layer 112 may also be configuredto support a “Grid Aware” plug-in hybrid electric vehicle(PHEV)/electric vehicle charging system instead of (or in addition to)having the charging system in the vehicles be grid-aware. For example,in buildings that have vehicle charging stations (e.g., terminals in aparking lot for charging an electric or hybrid vehicle), the DR layer112 can decide when to charge the vehicles (e.g., when to enable thecharging stations, when to switch a relay providing power to thecharging stations, etc.) based upon time, real time pricing (RTP)information from the smart grid, or other pricing, demand, orcurtailment information from the smart grid. In other embodiments, eachvehicle owner could set a policy that is communicated to the chargingstation and back to the DR layer 112 via wired or wirelesscommunications that the DR layer 112 could be instructed to follow. Thepolicy information could be provided to the DR layer 112 via anenterprise application 124, a vehicle information system, or a personalportal (e.g., a web site vehicle owners are able to access to input, forexample, at what price they would like to enable charging). The DR layer112 could then activate the PHEV charging station based upon that policyunless a curtailment event is expected (or occurs) or unless the DRlayer 112 otherwise determines that charging should not occur (e.g.,decides that electrical storage should be conducted instead to help withupcoming anticipated peak demand). When such a decision is made, the DRlayer 112 may pre-charge the vehicle or suspend charge to the vehicle(e.g., via a data command to the charging station). Vehicle charging maybe restricted or turned off by the smart building manager during periodsof high energy use or expensive energy. Further, during such periods,the smart building manager 106 or the DR layer 112 may be configured tocause energy to be drawn from plugged-in connected vehicles tosupplement or to provide back-up power to grid energy.

Using the real time (or near real-time) detailed information regardingenergy use in the building, the smart building manager 106 may maintaina greenhouse gas inventory, forecast renewable energy use, surpluses,deficits, and generation, and facilitate emission allocation, emissiontrading, and the like. Due to the detailed and real-time or nearreal-time nature of such calculations, the smart building manager 106may include or be coupled to a micro-transaction emission tradingplatform.

The DR layer 112 may further be configured to facilitate the storage ofon-site electrical or thermal storage and to controllably shiftelectrical loads from peak to off peak times using the stored electricalor thermal storage. The DR layer 112 may be configured to significantlyshed loads during peak hours if, for example, high price or contractedcurtailment signals are received, using the stored electrical or thermalstorage and without significantly affecting building operation orcomfort. The integrated control layer 116 may be configured to use abuilding pre-cooling algorithm in the night or morning and rely oncalculated thermal storage characteristics for the building in order toreduce peak demand for cooling. Further, the integrated control layer116 may be configured to use inputs such as utility rates, type ofcooling equipment, occupancy schedule, building construction, climateconditions, upcoming weather events, and the like to make controldecisions (e.g., the extent to which to pre-cool, etc.).

Automated Measurement & Verification Layer

FIGS. 1A and 1B are further shown to include an automated measurementand validation layer 110 configured to evaluate building system (andsubsystem) performance. The automated measurement and validation (AM&V)layer 110 may implement various methods or standards of theinternational performance measurement and validation (IPMVP) protocol.In an exemplary embodiment, the AM&V layer 110 is configured toautomatically (e.g., using data aggregated by the AM&V layer 110,integrated control layer 116, building subsystem integration layer 118,FDD layer 114, or otherwise) verify the impact of the integrated controllayer 116, the FDD layer 114, the DR layer 112, or other energy-savingstrategies of the smart building manager 106. For example, the AM&Vlayer 110 may be used to validate energy savings obtained by capitalintensive retrofit projects that are monitored or managed post retrofitby the smart building manager. The AM&V layer 110 may be configured tocalculate, for example, a return on investment date, the money savedusing pricing information available from utilities, and the like. TheAM&V layer 110 may allow for user selection of the validation method(s)it uses. For example, the AM&V layer 110 may allow for the user toselect IPMVP Option C which specifies a method for the direct comparisonof monthly or daily energy use from a baseline model to actual data fromthe post-installation measurement period. IPMVP Option C, for example,may specify for adjustments to be made of the base-year energy modelanalysis to account for current year over base year changes inenergy-governing factors such as weather, metering period, occupancy, orproduction volumes. The AM&V layer 110 may be configured to track (e.g.,using received communications) the inputs for use by such a validationmethod at regular intervals and may be configured to make adjustments toan “adjusted baseline energy use” model against which to measuresavings. The AM&V layer 110 may further allow for manual or automaticnon-routine adjustments of factors such as changes to the facility size,building envelope, or major equipment. Algorithms according to IPMVPOption B or Option A may also or alternatively be used or included withthe AM&V layer 110. IPMVP Option B and IPMVP Option A involve measuringor calculating energy use of a system in isolation before and after itis retrofitted. Using the building subsystem integration layer (or otherlayers of the BMS), relevant data may be stored and the AM&V layer 110may be configured to track the parameters specified by IPMVP Option B orA for the computation of energy savings for a system in isolation (e.g.,flow rates, temperatures, power for a chiller, etc.).

The AM&V layer 110 may further be configured to verify that controlstrategies commanded by, for example, the integrated control layer orthe DR layer are working properly. Further, the AM&V layer 110 may beconfigured to verify that a building has fulfilled curtailment contractobligations. The AM&V layer 110 may further be configured as anindependent verification source for the energy supply company (utility).One concern of the utility is that a conventional smart meter may becompromised to report less energy (or energy consumed at the wrongtime). The AM&V layer 110 can be used to audit smart meter data (orother data used by the utility) by measuring energy consumption directlyfrom the building subsystems or knowledge of building subsystem usageand comparing the measurement or knowledge to the metered consumptiondata. If there is a discrepancy, the AM&V layer may be configured toreport the discrepancy directly to the utility. Because the AM&V layermay be continuously operational and automated (e.g., not based on amonthly or quarterly calculation), the AM&V layer may be configured toprovide verification of impact (e.g., of demand signals) on a granularscale (e.g., hourly, daily, weekly, etc.). For example, the AM&V layermay be configured to support the validation of very short curtailmentcontracts (e.g., drop X kW/h over 20 minutes starting at 2:00 pm) actedupon by the DR layer 112. The DR layer 112 may track meter data tocreate a subhourly baseline model against which to measure loadreductions. The model may be based on average load during a period ofhours prior to the curtailment event, during the five prior uncontrolleddays, or as specified by other contract requirements from a utility orcurtailment service provider (e.g., broker). The calculations made bythe AM&V layer 110 may be based on building system energy models and maybe driven by a combination of stipulated and measured input parametersto estimate, calculate, apportion, and/or plan for load reductionsresulting from the DR control activities.

The AM&V layer 110 may yet further be configured to calculate energysavings and peak demand reductions in accordance with standards,protocols, or best practices for enterprise accounting and reporting ongreenhouse gas (GHG) emissions. An application may access data providedor calculated by the AM&V layer 110 to provide for web-based graphicaluser interfaces or reports. The data underlying the GUIs or reports maybe checked by the AM&V layer 110 according to, for example, the GHGProtocol Corporate Accounting Standard and the GHG Protocol for ProjectAccounting. The AM&V layer 110 preferably consolidates data from all thepotential sources of GHG emissions at a building or campus andcalculates carbon credits, energy savings in dollars (or any othercurrency or unit of measure), makes adjustments to the calculations oroutputs based on any numbers of standards or methods, and createsdetailed accountings or inventories of GHG emissions or emissionreductions for each building. Such calculations and outputs may allowthe AM&V layer 110 to communicate with electronic trading platforms,contract partners, or other third parties in real time or near real timeto facilitate, for example, carbon offset trading and the like.

The AM&V Layer 110 may be further configured to become a “smart electricmeter” a or substitute for conventional electric meters. One reason theadoption rate of the “Smart Electric Grid” has conventionally been lowis that currently installed electric meters must be replaced so that themeters will support Real Time Pricing (RTP) of energy and other datacommunications features. The AM&V layer 110 can collect interval-basedelectric meter data and store the data within the system. The AM&V layer110 can also communicate with the utility to retrieve or otherwisereceive Real Time Pricing (RTP) signals or other pricing information andassociate the prices with the meter data. The utility can query thisinformation from the smart building manager (e.g., the AM&V layer 110,the DR layer 112) at the end of a billing period and charge the customerusing a RTP tariff or another mechanism. In this manner, the AM&V layer110 can be used as a “Smart Electric Meter”.

When the AM&V layer 110 is used in conjunction with the DR layer 112,building subsystem integration layer 118, and enterprise integrationlayer 108, the smart building manager 106 can be configured as an energyservice portal (ESP). As an ESP, the smart building manager 106 maycommunicably or functionally connect the smart grid (e.g., energy supplycompany, utility, ISO, broker, etc.) network to the metering and energymanagement devices in a building (e.g., devices built into appliancessuch as dishwashers or other “smart” appliances). In other words, thesmart building manager 106 may be configured to route messages to andfrom other data-aware (e.g., Real Time Pricing (RTP) aware, curtailmentsignal aware, pricing aware, etc.) devices and the energy supplycompany. In this configuration, building subsystems that are not RTPaware will be managed by the DR layer 112 while devices that are RTPaware can get signals directly from the utility. For example, if avehicle (e.g., PHEV) is programmed to charge only when the price ofelectricity is below $0.1/kWh, the PHEV can query the utility throughthe smart building manager and charge independently from the DR layer112.

In an exemplary embodiment the AM&V layer described in U.S. ProvisionalApplication No. 61/302,854, filed Feb. 9, 2010 can be used as AM&V layer110 or a part thereof.

Enterprise Integration Layer

The enterprise integration layer 108 shown in FIG. 1A or FIG. 1B isconfigured to serve clients or local applications with information andservices to support a variety of enterprise-level applications. Theenterprise integration layer 108 may be configured to communicate (inreal time or near real time) with the smart grid 104 and energyproviders and purchasers 102. More particularly, in some embodiments theenterprise integration layer 108 may communicate with “smart meters,”automated meter interfaces with utilities, carbon emission tracking andaccounting systems, energy reporting systems, a building occupantinterface, and traditional enterprise productivity applications (e.g.,maintenance management systems, financial systems, workplace and supplychain management systems, etc.). The enterprise integration layer 108may be configured to use protocols and methods as described above withrespect to other layers or otherwise.

Communication and Security Features

Referring again to FIG. 3, the smart building manager may be configuredto provide drivers for BACnet, LON, N2, Modbus, OPC, OBIX, MIG, SMTP,XML, Web services, and various other wireless communications protocolsincluding Zigbee. These drivers may be implemented within or used by theservice bus adapters or subsystem adapters. The service bus for thesmart building manager may be configured to communicate using any numberof smart grid communications standards. Such standards may be utilizedfor intra-manager communication as well as communication with a smartgrid component (e.g., utility company, smart meter, etc.). For example,the smart building manager may be configured to use the ANSIC12.22/C12.19 protocol for some internal communications (e.g., DRevents) as well as for communications with the smart grid. The servicebus adapters and subsystem adapters convert received messages into anormalized messaging format for use on the service bus. In an exemplaryembodiment the service bus is flexible, making use of IT-centric messagequeuing technologies (e.g., Open AMQ, MSMQ, and WebSphere MQ) to assurereliability, security, scalability, and performance. Service busadapters enable layers and applications to communicate among one anotherand/or to the various in-building or external systems (e.g., viasubsystem adapters). Stored communications rules may be used by theservice bus adapters, subsystem adapters, or other components of thesystem to catch or correct communications failures. Communications andaction-failure rules may also be configured for use by the action layersof the system. For example, the DR layer can check for whether an actionrequested or commanded by the DR layer has completed. If not, the DRlayer can take a different action or a corrective action (e.g., turn offan alternate load, adjust additional setpoints, trigger a focused FDDactivity, etc.) to ensure that DR needs are met. The smart buildingmanager can also determine if someone has provided a DR override commandto the system and take corrective action if available. If correctiveaction is unavailable, an appropriate message or warning may be sent toa DR partner (e.g., a utility co., an energy purchaser via the smartgrid, etc.).

The smart building manager 106 may reside on (e.g., be connected to) anIP Ethernet network utilizing standard network infrastructure protocolsand applications (e.g., DNS, DHCP, SNTP, SNMP, Active Directory, etc.)and can also be secured using IT security best practices for thosestandard network infrastructure protocols and applications. For example,in some embodiments the smart building manager may include or beinstalled “behind” infrastructure software or hardware such as firewallsor switches. Further, configurations in the smart building manager 106can be used by the system to adjust the level of security of the smartbuilding manager 106. For example, the smart building manager 106 (orparticular components thereof) can be configured to allow its middlelayers or other components to communicate only with each other, tocommunicate with a LAN, WAN, or Internet, to communicate with selectdevices having a building service, or to restrict communications withany of the above mentioned layers, components, data sources, networks,or devices. The smart building manager 106 may be configured to supporta tiered network architecture approach to communications which mayprovide for some measure of security. Outward facing components areplaced in a less secure “tier” of the network to act as a point of entryto/from the smart building manager 106. These outward facing componentsare minimized (e.g., a web server receives and handles all requests fromclient applications) which limits the number of ways the system can beaccessed and provides an indirect communications route between externaldevices, applications, and networks and the internal layers or modulesof the smart building manager 106. For example, “behind” the outwardfacing “first tier” may lie a more secure tier of the network thatrequires authentication and authorization to occur at the first tierbefore functions of the more secure tier are accessed. The smartbuilding manager 106 may be configured to include firewalls between suchtiers or to define such tiers to protect databases or core components ofthe system from direct unauthorized access from outside networks.

In addition to including or implementing “infrastructure” type securitymeasures as the type disclosed above, the smart building manager may beconfigured to include a communications security module configured toprovide network message security between the smart building manager andan outside device or application. For example, if SOAP messaging overHTTP is used for communication at the enterprise integration layer, theSOAP messages may be concatenated to include an RC2 encrypted headercontaining authentication credentials. The authentication credentialsmay be checked by the receiving device (e.g., the smart buildingmanager, the end application or device, etc.). In some embodiments theencrypted header may also contain information (e.g., bits) configured toidentify whether the message was tampered with during transmission, hasbeen spoofed, or is being “replayed” by an attacker. If a message doesnot conform to an expected format, or if any part of the authenticationfails, the smart building manager may be configured to reject themessage and any other unauthorized commands to the system. In someembodiments that use HTTP messages between the application and the smartbuilding manager, the smart building manager may be configured toprovide SSL for message content security (encryption) and/or Formsauthentication for message authentication.

The smart building manager 106 may yet further include an accesssecurity module that requires any application to be authenticated withuser credentials prior to logging into the system. The access securitymodule may be configured to complete a secure authentication challenge,accomplished via a public or private key exchange (e.g., RSA keys) of asession key (e.g., an RC2 key), after a login with user credentials. Thesession key is used to encrypt the user credentials for theauthentication challenge. After the authentication challenge, thesession key is used to encrypt the security header of the messages. Onceauthenticated, user actions within the system are restricted byaction-based authorizations and can be limited. For example, a user maybe able to command and control HVAC points, but may not be able tocommand and control Fire and Security points. Furthermore, actions of auser within the smart building manager are written to memory via anaudit trail engine, providing a record of the actions that were taken.The database component of the smart building manager 106 (e.g., forstoring device information, DR profiles, configuration data, pricinginformation, or other data mentioned herein or otherwise) can beaccessible via an SQL server that is a part of the building managementserver or located remotely from the smart building manager 106. Forexample, the database server component of the smart building manager 106may be physically separated from other smart building manager componentsand located in a more secure tier of the network (e.g., behind anotherfirewall). The smart building manager 106 may use SQL authentication forsecure access to one or more of the aforementioned databases.Furthermore, in an exemplary embodiment the smart building manager canbe configured to support the use of non-default instances of SQL and anon-default TCP port for SQL. The operating system of the smart buildingmanager may be a Windows-based operating system.

Each smart building manager 106 may provide its own security and is notreliant on a central server to provide the security. Further, the samerobustness of the smart building manager 106 that provides the abilityto incorporate new building subsystem communications standards, modules,drivers and the like also allows it to incorporate new and changingsecurity standards (e.g., for each module, at a higher level, etc.).

Multi-Campus/Multi-Building Energy Management

The smart building manager 106 shown in the Figures may be configured tosupport multi-campus or multi-building energy management services. Eachof a plurality of campuses can include a smart building managerconfigured to manage the building, IT, and energy resources of eachcampus. In such an example, the building subsystems shown, e.g, in FIGS.1A and 1B may be a collection of building subsystems for multiplebuildings in a campus. The smart building manager may be configured tobi-directionally communicate with on-site power generation systems(e.g., distributed power sources, related services, solar arrays, fuelcell arrays, diesel generators, combined heat and power (CHP) systems,etc.), plug-in hybrid electric vehicle (PHEV) systems, and energystorage systems (e.g., stationary energy storage, thermal energystorage, etc.). Data inputs from such sources may be used by the demandand response layer of the smart building manager to make demand orresponse decisions and to provide other ancillary services to aconnected smart grid (e.g., utility, smart meter connected to a utility,etc.) in real time or near real time. For example, the smart buildingmanager may communicate with smart meters associated with an energyutility and directly or indirectly with independent systems operators(ISOs) which may be regional power providers. Using thesecommunications, and its inputs from devices of the campus, the smartbuilding manager (e.g., the demand response layer) is configured toengage in “peak shaving,” “load shedding,” or “load balancing” programswhich provide financial incentives for reducing power draw duringcertain days or times of day. The demand response layer or other controlalgorithms of the smart building manager (e.g., control algorithms ofthe integrated control layer) may be configured to use weather forecastinformation to make setpoint or load shedding decisions (e.g., so thatcomfort of buildings in the campus is not compromised). The smartbuilding manager may be configured to use energy pricing information,campus energy use information, or other information to optimize businesstransactions (e.g., the purchase of energy from the smart grid, the saleof energy to the smart grid, the purchase or sale of carbon credits withenergy providers and purchasers, etc.). The smart building manager isconfigured to use the decisions and processing of the demand responselayer to affect control algorithms of the integrated control layer.

While FIG. 1B is shown as a tightly-coupled smart building manager 106,in some embodiments the processing circuit of FIG. 1B (including thelayers/modules thereof) may be distributed to different servers thattogether form the smart building manager having the control featuresdescribed herein. In embodiments where the smart building manager 106 iscontrolling an entire campus or set of campuses, one or more smartbuilding managers may be layered to effect hierarchical controlactivities. For example, an enterprise level smart building manager mayprovide overall DR strategy decisions to a plurality of lower levelsmart building managers that process the strategy decisions (e.g., usingthe framework shown in FIG. 3) to effect change at an individual campusor building. By way of further example, the “integrated control layer”116 and the “building system integration layer” 118 may be replicatedfor each building and stored within lower level smart building serverswhile a single enterprise level smart building manager may provide asingle higher level layer such the DR layer. Such a DR layer can executea campus-wide DR strategy by passing appropriate DR events to theseparate lower level smart building mangers having integrated controllayers and building system integration layers. Higher level servers mayprovide software interfaces (APIs) to the one or more lower levelservers so that the one or more lower level servers can requestinformation from the higher level server, provide commands to the higherlevel server, or otherwise communicate with the layers or data of thehigher level server. The reverse is also true, APIs or other softwareinterfaces of the lower level servers may be exposed for consumption bythe higher level server. The software interfaces may be web servicesinterfaces, relational database connections, or otherwise.

Statistical Process Control and Fault Detection Using Moving Averages

A moving average can be used as an input to a statistical processcontrol strategy for detecting a variation in the behavior of thebuilding management system. In general, moving averages are a class ofstatistical metrics that utilize previously calculated averages in theircomputation. Moving averages may advantageously reduce processing timesand memory requirements relative to other statistical processingstrategies, since only a subset of the data values needs to be retained.For example, a standard average may be calculated using the formula:

${avg}_{i} = \frac{\sum\limits_{i = 1}^{n}\; x_{i}}{i}$where i is the number of data points and x_(i) is the i^(th) data point.A standard average requires summing the data points each time a new datapoint is collected and requires retaining each data point in memory. Amoving average, by contrast, can use the previously calculated averageto generate a new average when x_(i+1) becomes available. For example, amoving average may be calculated using:

${mov\_ avg}_{i + 1} = \frac{x_{i + 1} + {i*{avg}_{i}}}{i + 1}$where x_(i+1) is the most recent data point and avg, is the previouslycomputed average.

Weighted moving averages are a subclass of moving averages that applyweightings to the various subsets of data. For example, a weightedmoving average may weight more recent data values higher than oldervalues. In this way, the weighted moving average provides a currentmetric on the underlying data. Exponentially weighted averages (EWMAs)have been used to diagnose faults in building management controllers.See, U.S. Pat. No. 5,682,329 to Seem et al. EWMAs utilize exponentialweightings that can be used to give greater emphasis to more recentvalues. A variety of equations exist for calculating an EWMA. Forexample, an EWMA may be calculated according to the following function:

$\overset{\_}{x_{t}} = {\sum\limits_{j = 0}^{\infty}\;{{\lambda\left( {1 - \lambda} \right)}^{j}x_{t - j}}}$where x _(t) is the EWMA at time t; λ is an exponential smoothingconstant or filter value; and x_(t−j) is the value of the signal at timet−j.

Embodiments of the present application can include using EWMA-basedcontrol strategies to detect errors. In one example relating to an HVACsystem, a building management system controller may sample the positionof a damper that it controls. The controller can then calculate the EWMAof the position value. If the EWMA exceeds a threshold, the controllermay determine that the damper is in a saturation condition. Thecontroller can then notify a user of the potential fault.

In another example, a network of controllers may collect EWMA values fora temperature error. A design criterion for the network may be set suchthat ninety five percent of all controllers should have a temperatureerror EWMA of below 2° F. An EWMA of the temperature error greater than2° F. could be used to estimate or predict system faults while an EWMAof less than 2° F. could indicate that the network is working properly.

A statistical process control strategy of varying exemplary embodimentsmay detect variations in measured data by evaluating the measured datarelative to a trained statistical model (e.g., a statistical processcontrol chart). The trained statistical model may be based onmeasurements taken during a training period (e.g., while the buildingmanagement system is operating normally, during a steady state operatingperiod, etc.). The trained statistical model is used to predict behaviorfor the building management system under normal operating conditions.Measured data that falls outside of the parameters of the statisticalmodel may be considered to be statistically significant and indicatethat the predicted behavior is no longer valid and/or that faults existin the building management system.

Referring now to FIG. 5A, a flow diagram of a process 500 for usingstatistical process control to detect faults in a building managementsystem is shown, according to an exemplary embodiment. Process 500includes collecting training data from the building management system(step 502). During the training period, training data (i.e. performancevalues) are collected to build a history of performance values. Forexample, the performance values may be measured temperature values,calculated error rates, moving averages, measured power consumptions, orany other historical performance data. The history of performance valuesis used to determine if the BMS is operating normally.

Once a sufficient history of performance values has been built, thehistory can be used to generate a statistical model (step 504).Generally speaking, the statistical model is a set of metrics based on,calculated using, or describing the history of performance values. Thestatistical model is used to predict a behavior of the BMS.

Process 500 is further shown to include calculating an EWMA using newperformance values (step 506). The new performance values are collectedafter the training period. In some embodiments, the new performancevalues are collected by building management controllers and sent via anetwork to a remote location for calculation of the EWMA. In otherembodiments, the EWMA is calculated directly on a local BMS controllerthat collects the performance values.

Process 500 is yet further shown to include comparing the EWMAcalculated in step 506 to the statistical model generated in step 504(step 508). For example, the EWMA calculated in step 506 can be comparedto the statistical model generated in step 504 to test for statisticalsignificance, i.e. if the EWMA is an outlier in relation to thestatistical model. If a specified number of outliers are detected, thesystem can determine that the newly observed behavior of the system nolonger matches the predicted behavior (i.e., described by thestatistical model of step 504) and that appropriate action is necessary.If the new performance value is determined to be statisticallysignificant in step 508, i.e. it is an outlier in relation to thestatistical model of behavior for that performance value, any number ofactions may be taken by the BMS and/or a user. For example, if process500 is used within fault detection and diagnostics (FDD) layer 114,statistical significance of new performance values may indicate that afault condition exists. FDD layer 114 may then notify a user, amaintenance scheduling system, or a control algorithm configured toattempt to further diagnose the fault, to repair the fault, or towork-around the fault. If process 500 is used in automated measurementand validation (AM&V) layer 110, a statistical significance of newperformance values may indicate that a predicted model of a powerconsumption is no longer valid. AM&V layer 110 may then attempt toupdate the statistical model to better predict future power consumptionsand/or notify FDD layer 114 that a fault condition may exist.

Referring now to FIG. 5B, a detailed diagram of a fault detection moduleis shown, according to an exemplary embodiment. Automated faultdetection module 412 includes EWMA generator 520, which receivesbuilding management system data such as meter data 402, weather data 404and building subsystem data 408. EWMA generator 520 calculatesexponentially weighted moving averages of the data or a value calculatedusing the data, and outputs them as performance indices 410. Performanceindices 410 may be stored in performance value database 524.

The EWMAs may be calculated directly on building equipment controllersnot shown in FIG. 5B and transmitted to automated fault detection module412 (e.g., via a network, via communications electronics, etc.). Inother embodiments, some EWMAs are calculated directly on the buildingequipment controllers, while others are calculated remotely by EWMAgenerator 520.

EWMA generator 520 may calculate the moving averages by first removingsudden spikes in the data by applying an anti-spike filter or an outlierfilter. For example, EWMA generator 520 may use a generalized extremestudentized distribution method to remove outliers in the buildingmanagement system data. EWMA generator 520 may also sub-sample thebuilding management system data to reduce the effects of autocorrelationin the data. For example, a sampling interval greater than or equal tothe time constant of the process being controlled by the buildingequipment controller may be used.

Automated fault detection module 412 includes performance value database524. Performance value database 524 can store a history of performancevalues used by training component 522 to generate statistical models,such as model data 406. In one embodiment, the history of performancevalues stored in performance value database 524 contains a record ofEWMAs previously calculated by EWMA generator 520. In anotherembodiment, performance value database 524 contains a history of rawdata values from the building management system.

Automated fault detection module 412 is further shown to includetraining component 522 which performs statistical operations on thehistory of performance values to produce one or more thresholdparameters as inputs to threshold evaluator 526. The thresholdparameters are statistical predictors of the behavior of the buildingmanagement system, i.e. markers that define a range of normal behaviorwithin a specific statistical confidence. For example, trainingcomponent 522 may generate threshold parameters that define a modelwherein 99.7% of values observed during the training period fall withinupper and lower temperature threshold parameters.

Automated fault detection module 412 is yet further shown to includethreshold parameter evaluator 526. Threshold parameter evaluator 526 canuse the one or more threshold parameters from training component 522 todetermine if new performance values are statistically significant. Forexample, threshold parameter evaluator 526 may compare a new EWMA fromEWMA generator 520 to a trained threshold parameter to determine if thenew EWMA is statistically significant, i.e. the new EWMA falls outsideof the predicted behavior. If a new performance value is statisticallysignificant, threshold parameter evaluator 526 may notify automateddiagnostics module 414, manual diagnostics module 416, and/or GUIservices that a possible fault condition exists. Additionally, a usermay be notified that a fault may exist by GUI services causing agraphical user interface to be displayed on an electronic displaydevice. The generated graphical user interface can include an indicatorthat a fault has occurred and information regarding the estimated fault.Threshold parameter evaluator 526 may notify automated diagnosticsmodule 414, manual diagnostics module 416, or GUI services only if aplurality of statistically significant performance values are detected.

Statistical Process Control Chart Generation

In many of the varying exemplary embodiments, the statistical model usedin the statistical process control strategy may be a statistical processcontrol chart (e.g., an EWMA control chart, etc.). Such charts typicallyutilize upper and lower control limits relative to a center line todefine the statistical boundaries for the process. New data values thatare outside of these boundaries indicate a deviation in the behavior ofthe process. In some cases, the charts may also contain one or morealarm thresholds that define separate alarm regions below the uppercontrol limit and above the lower control limits. A processor utilizingsuch a chart may determine that a new data value is within orapproaching an alarm region and generate an alert, initiate a diagnosticroutine, or perform another action to move the new data values away fromthe alarm regions and back towards the center line. Although thisdisclosure variously mentions the term “chart,” many of the exemplaryembodiments of the disclosure will operate without storing or displayinga graphical representation of a chart. In such embodiments, aninformation structure suitable for representing the data of astatistical process control chart may be created, maintained, updated,processed, and/or stored in memory. Description in this disclosure thatrelates to systems having statistical process control charts orprocesses acting on or with statistical process control charts isintended to encompass systems and methods that include or act on suchsuitable information structures.

Referring now to FIG. 6A, a flow diagram of a process for generating astatistical process control chart is shown, according to an exemplaryembodiment. Process 600 includes receiving a history of performancevalues (step 602). The performance values may be data collected by theBMS during normal operations. For example, the performance values may bemeasured temperature values, calculated error rates, measured powerconsumptions, or any other data that can be used to determine whetherthe BMS is operating normally. In another embodiment, the performancevalues are exponentially weighted moving averages of data from the BMS.A history of 150-500 performance values may be sufficient to create thestatistical model, although more or less may be used in varyingexemplary embodiments.

Process 600 is also shown to include generating a target parameter (step604). The target parameter provides a target metric for the system undernormal operating conditions. In one embodiment, the target parameter isthe statistical mean of the history of performance values from step 602,i.e. the simple average of the performance values. In anotherembodiment, the median of the history is used. In yet anotherembodiment, a moving average of the performance values can be used. Forexample, the history of performance values may be measured temperaturevalues that range from 95° F. to 105° F., with a simple average of 100°F. Therefore, a target parameter of 100° F. may be used to predictfuture temperatures for a normally operating BMS. Future performancevalues that vary greatly from the target parameter may indicate a faultin the BMS, a change in behavior of the BMS, or that the statisticalmodel needs to be updated.

Process 600 is further shown to include generating an estimator of scale(step 606). Estimators of scale generally provide a metric thatdescribes how spread out a set of performance values is relative to thetarget parameter. In one embodiment, the standard deviation of thehistory of performance values is calculated using the target parameterfrom step 604 and the performance values from step 602. For example, thehistory of performance values may contain measured temperatures thatrange from 95° F. to 105° F., with a simple average of 100° F. Assumingthat the performance values are distributed normally, i.e. they conformto a Gaussian distribution, a calculated standard deviation of 1.5° F.indicates that approximately 99.7% of the measured temperatures fallwithin the range of 95.5° F. to 104.5° F. However, a non-normaldistribution of the performance values or the presence of outlierperformance values can affect the ability of a standard deviation togauge the spread of the data.

In a preferred embodiment, a robust estimator of scale is calculated instep 606. Robust estimators of scale differ from standard estimators ofscale, such as a standard deviation, by reducing the effects of outlyingperformance values. A variety of different types of robust estimators ofscale may be used in conjunction with the present invention. Forexample, a robust estimator of scale that uses a pairwise differenceapproach may be used. Such approaches typically have a higher Gaussianefficiency than other robust approaches. These approaches provide auseful metric on the interpoint distances between elements of two arraysand can be used to compare a predicted behavior and an observed behaviorin the building management system. For example, one robust estimator ofscale may be defined as:S_(n)=c_(n)*1.1926*med_(i){med_(j)(|x_(i)−x_(j)|) where the set ofmedians for j=1, . . . , n is first calculated as an inner operation.Next, the median of these results is calculated with respect to the ivalues. The median result is then multiplied by 1.1926, to provideconsistency at normal distributions. A correction factor c_(n) may alsobe applied and is typically defined as 1 if n is even. If n is odd,c_(n) can be calculated as:

$c_{n} = {\frac{n}{n - 0.9}.}$The described S_(n) estimator of scale has a Gaussian efficiency ofapproximately 58%. Computational techniques are also known that computeS_(n) in O(n log n) time.

In another exemplary embodiment, Q_(n) may be used as a robust estimatorof scale, where Q_(n) is defined as Q_(n)=d_(n)*2.2219*1st quartile(|x_(i)−x_(j)|:i<j). As with S_(n), a pairwise difference approach istaken to compute Q_(n). If n is even, correction factor d_(n) can bedefined as:

$d_{n} = \frac{n}{n + 1.4}$and if n is odd, correction factor d_(n) can be defined as:

$d_{n} = {\frac{n}{n + 3.8}.}$The Qn estimator of scale provides approximately an 82% Gaussianefficiency and can also be computed in O(n log n) time.

Process 600 is yet further shown to include generating a thresholdparameter (step 608). In some embodiments, the threshold may be based onthe estimator of scale from step 606. For example, the thresholdparameters may be calculated using: threshold=Ξ±K*σ where K is aconstant, μ is the target parameter and σ is the estimator of scale.

A threshold parameter can be compared against a new performance valuefrom the BMS to determine whether the new performance value isstatistical significant. For example, if the history of performancevalues are measured temperatures that range from 95° F. to 105° F. witha simple average of 100° F. and a standard deviation of 1.5° F., K maybe set to 3 to provide an upper threshold parameter of 104.5° F.Assuming that the data values are normally distributed, this means thatapproximately 99.85% of the historical temperatures fall below thisthreshold parameter. New temperature measurements that are equal to orgreater than the threshold parameter may be statistically significantand indicate that a fault condition exists.

Referring now to FIG. 6B, a more detailed flow diagram of a process forgenerating a statistical process control chart is shown, according to anexemplary embodiment. Process 620 includes receiving a history ofperformance values from the BMS (step 622). In one embodiment, thehistory of performance values is built during a training period wherethe BMS is operating normally.

Process 620 is also shown to include checking the history of performancevalues for autocorrelation (step 624). In general, autocorrelationmeasures how closely a newer set of performance values follows thepattern of previous performance values. Any known method of testingautocorrelation may be used in step 624. For example, a lag-onecorrelation coefficient may be calculated to test for autocorrelation.If the coefficient is high, the data is assumed to be autocorrelated. Ifthe coefficient is low, the data is assumed not to be autocorrelated.

Process 620 optionally includes checking the history of performancevalues for normality, i.e how closely the history conforms to a Gaussiandistribution (step 626). Any suitable method of testing for normalitymay be used in step 626. For example, a Lillifors test may be used totest the null hypothesis that the data is normal against the alternativehypothesis that the data is not normal.

Process 620 is further shown to include generating a target parameterusing the history of performance values (step 628). The characteristicsof the history tested in steps 624 and 626 can be used to determine howthe target parameter is generated. Any suitable metrics that reduce theeffects of autocorrelation and non-normal data may be used. For example,if the history is determined not to be autocorrelated in step 624, themedian of the history may be used as the target parameter. In otherexamples, the EWMA or the simple mean of the history is used.

If the data is determined to be autocorrelated in step 624, anautoregressive model may be used to fit the data and the residuals usedto calculate the target parameter. For example, an AR(1) model may befit to the history using the equation: x_(t)=φ₀+φ₁*x_(t−1)+e_(t) where xis a predicted value, x_(t−1) is a previous value, φ₀ and φ₁ areconstants and e_(t) is a residual. The target parameter can then becalculated using the residual values. For example, the target parametercan be the simple mean of the residual values. In other embodiments, thetarget parameter can be the median of the residual values, a movingaverage of the residual values, or any other statistical metric thatgenerally corresponds to the center of the distribution of residualvalues.

Process 620 is yet further shown to include generating an estimator ofscale (step 630). The estimator of scale may be generated using thetarget parameter and/or the history of performance values. The type ofestimator of scale that is used may be determined based on the resultsof steps 624, 626 and/or the type of target parameter used in step 528.If the target parameter of step 628 is the median of the history and thehistory is not autocorrelated, a robust estimator of scale may be foundfor the history itself. However, if the data is autocorrelated and thetarget parameter is determined using an autoregressive model, a robustestimator of scale may calculated using the residuals of theautoregressive model. In other embodiments, other types of estimators ofscale are used, such as a standard deviation.

Process 620 is also shown to include generating a threshold parameter(step 632). The threshold parameter may be calculated using theestimator of scale of step 626 and the target parameter of step 628. Insome embodiments, the threshold parameter is calculated by multiplyingthe estimator of scale by a constant and adding or subtracting thatvalue from the target parameter, as in step 606 of method 600. Forexample, if the estimator of scale is a simple standard deviation, theconstant may be set to 3 to generate upper and lower thresholds thatencompass approximately 99.7% of the history. In this way, the choice ofa constant value may be used to define any number of thresholdparameters. In one embodiment, the threshold parameter is calculatedautomatically by the BMS. In another embodiment, a user may input adesired threshold parameter using a display device configured to receiveuser input. In yet another embodiment, a hybrid approach may be takenwhere the BMS automatically calculates the threshold parameter andprovided it to a display device for user confirmation of the thresholdparameter or input of a different threshold parameter.

The target parameter and one or more threshold parameters generated inprocess 600 or process 620 may also be used to generate a SPC controlchart. In such a case, the target parameter may be used as the centerline of the chart. The threshold parameters may also be used as upperand lower control limits for the SPC control chart. New data that fallsoutside of the control limits of the chart may indicate a deviation inthe behavior of the associated process.

Referring now to FIG. 7, a detailed diagram of training module 522 isshown, according to an exemplary embodiment. Training module 522 isshown to include performance value aggregator 702 which generates andmaintains a history of performance values. During training, performancevalue aggregator 702 stores performance values from the BMS asperformance indices 410 or in performance value database 524.Performance value aggregator 702 may also be configured to receive aninput from automated diagnostics module 414, manual diagnostics module416 or GUI services 422 that indicates that the system needs to beretrained. If retraining is needed, performance value aggregator 702 canupdate and store new performance values during the new training period.During a training period, performance value aggregator 702 can overwritesome data or delete some data (e.g., old data, faulty data, etc.) fromthe its performance value calculation. Once a sufficient number ofperformance values are collected, the training period ends andperformance value aggregator retrieves a history of performance valuesfrom performance indices 410 or performance value database 524.

Training module 522 includes autocorrelation evaluator 704.Autocorrelation evaluator 704 detects autocorrelation in the history ofperformance values retrieved by performance value aggregator 702. Forexample, autocorrelation evaluator 704 may use a lag-one correlationcoefficient method to test for autocorrelation in the history. Theresults of this determination are then provided to target parametergenerator 708, and may be used to determine the method to be used ingenerating the target parameter.

Training module 522 includes normality evaluator 706. Normalityevaluator determines how closely the history of performance valuesconforms to a Gaussian distribution, i.e. a bell-curve. Normal dataprovides a greater statistical confidence in the model's ability topredict behavior, although non-normal data can still be used to detectvariations in the system's behavior.

Training module 522 is further shown to include target parametergenerator 708 which uses the history of performance values fromperformance value aggregator 702 and the outputs of autocorrelationevaluator 704 and normality evaluator 706 to generate a targetparameter. The target parameter provides a statistical center for thestatistical model based on the history of performance values. Forexample, target parameter generator 708 may calculate the median of thehistory of performance values as the target parameter. Once targetparameter generator 708 generates a target parameter, an estimator ofscale is calculated by estimator of scale generator 710. Estimator ofscale generator 708 uses the output of target parameter generator 708and the history of performance values from performance value aggregator702 to generate an estimator of scale for the history, i.e. a metric onhow spread out the distribution of data is. In one embodiment, a robustestimator of scale is calculated by estimator of scale generator 710.

Training module 522 yet further includes threshold parameter generator712, which uses the outputs of target parameter generator 708 andestimator of scale generator 710 to generate one or more thresholdparameters. The one or more threshold parameters are then provided tothreshold parameter evaluator 712 for comparison against new performancevalues.

Statistical Process Control to Measure and Verify Energy Savings

Referring now to FIG. 8, a process 800 for measuring and verifyingenergy savings in a building management system is shown, according to anexemplary embodiment. Process 800 may be used by automated measurementand verification layer 110 to measure and verify energy savings in thebuilding management system. Process 800 is shown to include retrievinghistorical building and building environment data from a pre-retrofitperiod (step 802). Input variables retrieved in step 802 and used insubsequent steps may include both controllable variables (e.g.,variables that may be controlled by a user such as occupancy of an area,space usage, occupancy hours, etc.) and uncontrollable variables (e.g.,outdoor temperature, solar intensity and duration, other weatheroccurrences, degree days, etc.). Variables which are not needed (i.e.,they do not have an impact on the energy savings calculations) may bediscarded or ignored by automated measurement and verification layer110.

Process 800 is also shown to include using historical data to create abaseline model that allows energy usage (e.g., kWh) or power consumption(e.g., kW) to be predicted from varying input or predictor variables(e.g., occupancy, space usage, occupancy hours, outdoor air temperature,solar intensity, degree days, etc.). For example, power consumptionsmeasured during previous weekends may be used to predict future weekendpower consumptions, since the building is likely at minimum occupancyduring these times.

Process 800 is further shown to include storing agreed-upon ranges ofcontrollable input variables and other agreement terms in memory (step806). These stored and agreed-upon ranges or terms may be used asbaseline model assumptions. In other embodiments the baseline model or aresultant contract outcome may be shifted or changed when agreed-uponterms are not met.

Process 800 is yet further shown to include conducting an energyefficient retrofit of a building environment (step 808). The energyefficient retrofit may include any one or more process or equipmentchanges or upgrades expected to result in reduced energy consumption bya building. For example, an energy efficient air handling unit having aself-optimizing controller may be installed in a building in place of alegacy air handling unit with a conventional controller. Once the energyefficient retrofit is installed, a measured energy consumption for thebuilding is obtained (step 810). The post-retrofit energy consumptionmay be measured by a utility provider (e.g., power company), a system ordevice configured to calculate energy expended by the building HVACsystem, or otherwise.

Process 800 also includes applying actual input variables of thepost-retrofit period to the previously created baseline model to predictenergy usage of the old system during the post-retrofit period (step812). This step results in obtaining a baseline energy consumption(e.g., in kWh) against which actual measured consumption from theretrofit can be compared.

Process 800 is further shown to include subtracting the measuredconsumption from the baseline energy consumption to determine potentialenergy savings (step 814). In an exemplary embodiment, a baseline energyconsumption is compared to a measured consumption in by subtracting themeasured consumption during the post-retrofit period from the baselineenergy consumption calculated in step 812. This subtraction will yieldthe energy savings resulting from the retrofit.

Process 800 is yet further shown to include checking the baseline modelassumptions for changes by comparing the calculated energy savings to athreshold parameter (step 816). For example, an EWMA control chart maybe applied to the calculated energy savings to check the validity of themodel assumptions. Such a chart may utilize control limits (e.g.,threshold parameters) generated using a computerized implementation ofprocess 600 or 620. A BMS implementing process 800 may determine if thesavings are outside of the control limits of the chart. If the savingsare outside of the control limits, the BMS may then generate an alert ormay initiate other corrective measures. For example, the BMS may thendetermine new baseline model assumptions (e.g., by repeating step 806)and repeating steps 808-816 to continuously calculate and verify thepotential energy savings for the building.

Referring now to FIG. 9, a detailed diagram of a building managementsystem portion is shown, according to an exemplary embodiment. The logicblocks shown in FIG. 9 may represent software modules of fault detectionand diagnostics layer 114 shown in FIG. 1. Field controller 904 controlsone or more components of the BMS and receives or calculates performancevalues 906 (e.g., sensor inputs, actuator positions, etc.). Controller904 can store a trend of performance values 906, setpoints and currentstatus in local trend storage 908. Trend storage 908 may be a memorydevice that is a component of, coupled to, or located externally tocontroller 904. In one embodiment, the trend sample intervals used tosample performance values 906 are setup during a system configurationprocess. For example, the sample intervals may be less than one half ofthe time constant of the process controlled by controller 904 to preventaliasing. Other sample intervals may also be used, depending upon thetype of data that is sampled.

The trend data in local trend storage 908 may be communicated overnetwork 912 (e.g., the Internet, a WAN, a LAN, etc.) to an EWMA database924 or to an intermediate server between controller 904 and EWMAdatabase 924. In one embodiment, the trend data from local trend storage908 may be provided to delay filter 914. Delay filter 914 removes datathat is likely to contain excessive field controller dynamics.Typically, the delay period for delay filter 914 is greater than orequal to five times the time constant of the process controlled bycontroller 904, although other delay periods may also be used. In someembodiments, delay filter 914 is triggered by a change in the currentstatus of controller 904 or by changes in one or more setpoint valuesfor controller 904.

A performance index calculator 916 may use the outputs of delay filter914 to calculate a performance index. For example, performance indexcalculator 916 may use the setpoint of controller 904 minus performancevalues 906 to determine a performance index. Once a performance indexhas been calculated by performance index calculator 916, outlier remover918 may be used to remove anomalous values. For example, outlier remover918 may utilize the generalized extreme studentized deviate (GESD)method or an anti-spike filter to remove extreme data values.

EWMA calculator 920 may calculate a moving average of the data fromoutlier remover 918, which is sub-sampled by sampler 923. Sampler 923samples the EWMA data to remove or reduce autocorrelation. For example,sampler 923 may utilize a sample interval greater than or equal to fivetimes the time constant of the process controlled by controller 904 tosample the EWMA data, although other sampler intervals may also be used.

In other embodiments, EWMAs may be calculated directly in a controller,such as field controller 902. Field controller 902 receives performancevalues 905 from the controlled process (e.g., measured temperaturevalues, measured power consumptions, or any other data that can be usedto determine if the BMS is operating normally). The EWMA data is thentrended and stored in trend storage 919. Trend storage 919 may be amemory local to controller 902, a memory in a supervisory controllerhaving control over controller 902, or within any other device withinthe BMS. Typically, the trend sample interval time for trend storage 919is set up during system configuration and ranges from 1-60 minutes,although other interval times may also be used.

The trended EWMA data in trend storage 919 is transmitted over network912 to outlier remover 921, which filters outliers from the data. Forexample, outlier remover 921 may use the GESD method, an anti-spikefilter, or another method capable of removing outliers from the data.Outlier remover 921 provides the resultant data to sampler 922, whichsub-samples the data to remove or reduce autocorrelation. Sampler 922may utilize a sample interval greater than or equal to five times thetime constant of the process controlled by controller 902 to sample theEWMA data, although other sampler intervals may also be used. SampledEWMA data from samplers 922, 923 are then stored in EWMA database 924 asa history of EWMA values. In this way, EWMA database 924 may be used totrain or test EWMA with a statistical process control chart.

Using EWMA database 924, the BMS may determine an analysis period orschedule and determine if training has not been performed or ifretraining has been triggered (step 926). If training or retraining isnecessary, the BMS may then determine if a desirable set of trainingdata is available (step 928). For example, training sets of 150-500 datapoints are typically used. Other amounts of training data may also beused, so long as they provide a sufficient history of behavior of theBMS. If an insufficient amount of data has yet to be collected, the BMSmay continue to collect data until reaching a desired amount.

If EWMA database 924 contains a sufficient amount of training data, theBMS may implement process 930 to define a statistical process controlchart. Process 930 includes checking the autocorrelation and setting astatistical process control chart method (step 932). For example,autocorrelation may be checked by calculating a lag one correlationcoefficient. If the coefficient is low, the data is not autocorrelatedand an EWMA method may be used. If the coefficient is high, the data isconsidered to be autocorrelated and an AR method may be used. Under theAR method, an AR-one model may first be fit to the training data. TheAR-res (residuals) of the AR-one model may then be used in other stepsof process 930.

Process 930 is shown to include checking the data for normality (step934). In general, normal data provides better performance thannon-normal data. However, non-normal data may also be used to detectchanges in the behavior of the BMS. Normality may be tested using aLillifors test or any other normality test capable of distinguishingnormal data sets from non-normal data sets.

Process 930 further includes calculating robust estimates of the targetparameter (μ) and the estimator of scale (σ) (step 936). In oneembodiment, the target parameter is the statistical mean of the historyof the EWMA. For example, the simple mean of the data may be calculatedif the data is determined to be normal in step 934. In anotherembodiment, the median of the data is used. In a preferred embodiment,the estimator of scale calculated in step 936 is a robust estimator ofscale, although other estimators may also be used. For example, robustestimators of scale having Gaussian efficiencies of about 58% or about82% may be used.

Process 930 yet further includes calculating the control chart limits(i.e., the one or more threshold parameters) (step 938). For example, anupper control limit (UCL) may be calculated by multiplying the estimatorof scale by a constant value K and adding the result to the targetparameter. In another example, the product of the constant and theestimator of scale may be subtracted by the target parameter to generatea lower control limit (LCL).

Once target parameters have been established using process 930, the BMScan begin to use the generated statistical process control chart todetect changes in the behavior of the BMS. If new EWMA or AR-res valuesare less than the LCL or greater than the UCL, the new values areconsidered to be outliers (e.g., one or more statistically significantoutliers) (step 940). Optionally, the BMS also determines if anexcessive number of outliers have been detected (step 942). For example,the BMS may disregard one or more outliers detected in step 942 beforetaking further action. The number of outliers necessary before takingfurther action may be set manually by a user or automatically by the BMSitself. For example, the BMS may utilize data concerning the operationalstate of controller 902 to determine a threshold number of outliers.

If the BMS determines in step 942 that an excessive number of outliershave been detected, the BMS may present an indication to a user via adisplay device (step 944). Alternatively, or in addition to step 944,the BMS may take any number of other measures if a change in behaviorhas been detected. For example, the BMS may initiate a diagnosticsroutine, send a communication to a technician (e.g., email, textmessage, pager message, etc.), retrain the statistical model, or anyother appropriate action that corresponds to the change in behavior.

Configurations of Various Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, orientations,etc.). For example, the position of elements may be reversed orotherwise varied and the nature or number of discrete elements orpositions may be altered or varied. Accordingly, all such modificationsare intended to be included within the scope of the present disclosure.The order or sequence of any process or method steps may be varied orre-sequenced according to alternative embodiments. Other substitutions,modifications, changes, and omissions may be made in the design,operating conditions and arrangement of the exemplary embodimentswithout departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on memory or other machine-readable media for accomplishingvarious operations. The embodiments of the present disclosure may beimplemented using existing computer processors, or by a special purposecomputer processor for an appropriate system, incorporated for this oranother purpose, or by a hardwired system. Embodiments within the scopeof the present disclosure include program products or memory comprisingmachine-readable media for carrying or having machine-executableinstructions or data structures stored thereon. Such machine-readablemedia can be any available media that can be accessed by a generalpurpose or special purpose computer or other machine with a processor.By way of example, such machine-readable media can comprise RAM, ROM,EPROM, EEPROM, CD-ROM or other optical disk storage, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to carry or store desired program code in the form ofmachine-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer or othermachine with a processor. Combinations of the above are also includedwithin the scope of machine-readable media. Machine-executableinstructions include, for example, instructions and data which cause ageneral purpose computer, special purpose computer, or special purposeprocessing machines to perform a certain function or group of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

The invention claimed is:
 1. A controller for a building managementsystem comprising: a processing circuit configured to at least one ofreceive or calculate an updated performance value for the buildingmanagement system; wherein the processing circuit is further configuredto maintain at least one threshold parameter relative to a history ofperformance values, to determine an exponentially weighted movingaverage of the performance values, and to compare the moving average tothe at least one threshold parameter, wherein the threshold parameter ismaintained by multiplying an estimator of scale of the history by aconstant value and at least one of adding or subtracting a result of themultiplication to or from a median of the history; and wherein theprocessing circuit is further configured to generate an output thatindicates a result of the comparison.
 2. The controller of claim 1,wherein the processing circuit is further configured to update thethreshold parameter automatically and without user input.
 3. Thecontroller of claim 1, wherein the threshold parameter is a thresholdfor a predicted power consumption and the performance values comprisemeasured power consumptions.
 4. The controller of claim 1, wherein theprocessing circuit is further configured to cause a graphical userinterface to be displayed on an electronic display device, wherein thegraphical user interface comprises indicia that a fault has occurred. 5.A method of detecting faults in a building management system, the methodcomprising: at a computer of the building management system, receivingfirst performance values for the building management system; calculatingan exponentially weighted moving average using the first performancevalues; generating a threshold parameter by multiplying an estimator ofscale of the first performance values by a constant value and at leastone of adding or subtracting a result of the multiplication to or from amedian of the first performance values; comparing the moving average tothe threshold parameter; receiving new performance values for thebuilding management system; and updating the threshold parameter usingthe new performance values.
 6. The method of claim 5, wherein updatingthe threshold parameter comprises: generating a target parameter,wherein the target parameter is a median of a history of performancevalues; generating an estimator of scale of the history, wherein theestimator of scale reduces an effect of outliers in the history; usingthe estimator of scale and the target parameter to generate thethreshold parameter; and storing the threshold parameter in a memory ofthe computer.
 7. The method of claim 6, wherein using the computer togenerate at least one threshold parameter comprises: determining if thehistory is autocorrelated; applying an autoregressive model to thehistory; and using the autoregressive model to generate the thresholdparameter.
 8. The method of claim 5, further comprising: updating thethreshold parameter automatically and without user input in response toa result of the comparison.
 9. The method of claim 5, wherein updatingthe threshold parameter using the new performance values furthercomprises: applying an anti-spike filter to the new performance values.10. The method of claim 5, wherein the new performance values aremeasured power consumptions and the at least one threshold parameter isselected based on a model of a predicted power consumption.